A photovoltaic power output optimization control method that integrates peak and valley electricity price time characteristics

Abstract

This invention discloses a photovoltaic power output optimization control method that integrates peak-valley electricity price period characteristics, comprising the following steps: establishing and running a power generation-electricity price coupling model to generate power allocation strategies and inverter operating parameters for the east and west photovoltaic arrays; real-time monitoring of the irradiance and power generation of the photovoltaic arrays; dynamically adjusting the power output of the east and west photovoltaic arrays to match the power generation curve with the peak-valley electricity price period to maximize power generation revenue; automatically adjusting the string connection status of the east and west photovoltaic arrays based on real-time irradiance changes and shading conditions using a dynamic string switching method; and periodically updating the power generation-electricity price coupling model. This invention has the following advantages and effects: by dynamically optimizing photovoltaic power output to accurately match peak-valley electricity price periods and adaptively adjusting the string operating status, it significantly improves the overall power generation revenue of the photovoltaic power plant.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power generation technology, and in particular to a photovoltaic power output optimization control method that integrates peak and valley electricity price time characteristics. Background Technology

[0002] With the rapid development of photovoltaic (PV) power generation technology and the widespread application of time-of-use (TOU) pricing policies, maximizing PV power generation revenue has become a key issue in power plant operation. TOU policies divide the day into peak, off-peak, and low-price periods, with higher prices during peak hours and lower prices during off-peak hours. PV power output is significantly intermittent and volatile due to factors such as irradiance, weather conditions, and array layout, leading to a mismatch between the power generation curve and the price curve. For example, in an asymmetrical east-west oriented PV array, the eastern array generates more power in the morning, while the western array generates more in the afternoon. However, peak electricity prices may occur during periods of high demand in the morning and evening, resulting in insufficient PV output during high-price periods and excessive output during low-price periods, thus reducing overall power generation revenue. Existing PV power control methods primarily focus on maximum power point tracking (MPPT) or local shading optimization, but lack deep integration with TOU policies, failing to dynamically adjust power output to match pricing periods. Furthermore, traditional methods struggle to cope with real-time irradiance variations and shading, leading to low power generation efficiency and revenue loss. Therefore, under the time-of-use pricing environment, how to optimize control to match the photovoltaic power generation curve with the peak and off-peak electricity price periods has become the core issue for improving power generation revenue. Summary of the Invention

[0003] The purpose of this invention is to provide a photovoltaic power output optimization control method that integrates peak and valley electricity price time characteristics to solve the problems mentioned in the background art.

[0004] The above-mentioned technical objective of the present invention is achieved through the following technical solution:

[0005] A photovoltaic power output optimization control method that integrates peak-valley electricity price time characteristics includes the following steps:

[0006] Based on the local power grid's peak-valley electricity price period division and price information, and combined with the east-west asymmetric layout characteristics of the photovoltaic array, a power generation-electricity price coupling model is established and run to generate power allocation strategies and inverter operating parameters for the east and west photovoltaic arrays; wherein, the peak-valley electricity price period includes peak period, flat period and low period;

[0007] The irradiance and power generation of the photovoltaic array are monitored in real time; wherein the photovoltaic array is composed of multiple strings, the irradiance is obtained through an irradiance sensor, and the power generation is obtained through string-level current and voltage sensors;

[0008] Based on the generated power allocation strategy and inverter operating parameters, and combined with real-time monitoring of irradiance and power generation, the power output of the photovoltaic arrays on the east and west sides is dynamically adjusted to match the power generation curve with the peak and valley electricity price period, so as to maximize power generation revenue.

[0009] The dynamic string switching method automatically adjusts the string connection status of the photovoltaic arrays on the east and west sides based on real-time irradiance changes and shading conditions. The dynamic string switching is based on a short-term irradiance prediction algorithm to predict future power generation capacity, and automatically cuts off the affected strings when irradiance drops significantly or shading occurs, and automatically reconnects them after the light conditions are restored.

[0010] The power generation-electricity price coupling model is updated regularly to reflect changes in electricity price policies and the performance degradation of photovoltaic arrays, and sensitivity analysis is conducted to assess the impact of the narrowing peak-valley price difference on power generation revenue.

[0011] By adopting the above technical solution and establishing and operating a power generation-electricity price coupling model, the power allocation strategy and inverter operating parameters for the east and west photovoltaic arrays can be generated based on the local power grid's peak-valley electricity price period division and electricity price information, combined with the east-west asymmetric layout characteristics of the photovoltaic array. This allows the photovoltaic power generation system to proactively adapt to electricity price changes, maximizing power output during peak hours and rationally controlling power generation during off-peak hours, avoiding resource waste and significantly improving power generation revenue. Real-time monitoring of the photovoltaic array's irradiance and power generation, through deployed irradiance sensors and string-level current and voltage sensors, provides accurate environmental and operational data, offering a reliable basis for dynamic control and ensuring timely and accurate system response. Based on the generated power allocation strategy and inverter operating parameters, combined with real-time monitoring data, the power output of the east and west photovoltaic arrays is dynamically adjusted to maximize power generation. The curve is highly aligned with peak-valley electricity price periods, maximizing power generation revenue and resolving the disconnect between power generation and electricity prices in traditional methods. Through a dynamic string switching method, the connection status of the strings in the east and west photovoltaic arrays is automatically adjusted based on real-time irradiance changes and shading conditions. A short-term irradiance prediction algorithm forecasts future power generation capacity, automatically cutting off affected strings when irradiance significantly decreases or shading occurs, and automatically reconnecting them once irradiance conditions recover. This effectively reduces the impact of local shading or irradiance fluctuations on overall power generation efficiency, improving system stability and efficiency. The power generation-price coupling model is regularly updated to reflect changes in electricity price policies and photovoltaic array performance degradation, and sensitivity analysis is performed to assess the impact of narrowing peak-valley price differences on power generation revenue. This ensures the system maintains an optimized state in the long term, adapts to changes in the external environment, maintains high profitability, and extends the economic lifespan of the photovoltaic power plant.

[0012] Further steps involve establishing and running a power generation-price coupling model based on the local power grid's peak-valley electricity price time period division and price information, combined with the east-west asymmetric layout characteristics of the photovoltaic array. This model generates the power allocation strategy for the east and west photovoltaic arrays and the inverter operating parameters. Specific steps include:

[0013] Obtain time-of-use pricing policy documents from local power grid operators or electricity market platforms, analyze and extract the start and end times and corresponding electricity values ​​for peak, flat, and off-peak periods, and generate electricity price curves;

[0014] Based on historical irradiance data and photovoltaic module performance parameters, the hourly power generation curves of the photovoltaic arrays on the east and west sides under different seasons and weather conditions are simulated; wherein, the historical irradiance data comes from local meteorological stations or satellite irradiance databases, and the photovoltaic module performance parameters include module efficiency, temperature coefficient and degradation rate;

[0015] The power generation curves simulated by the east and west photovoltaic arrays are overlaid with the electricity price curves over time. With the goal of maximizing net revenue or minimizing the cost per kilowatt-hour over the project's entire lifecycle, an optimization algorithm is used to generate initial power allocation strategies and inverter operating parameters for the east and west photovoltaic arrays. The power allocation strategy includes power output distribution schemes for the east and west photovoltaic arrays during peak, off-peak, and low-peak hours. The inverter operating parameters include the inverter's maximum power point tracking voltage range and output power limit. The optimization algorithm includes genetic algorithms, particle swarm optimization, or gradient descent algorithms.

[0016] By adopting the above technical solutions, time-of-use pricing policy documents are obtained from local grid operators or electricity market platforms. The start and end times and corresponding electricity values ​​for peak, flat, and off-peak periods are analyzed and extracted to generate electricity price curves, ensuring the accuracy and timeliness of electricity price data and providing a reliable foundation for subsequent optimization. Based on historical irradiance data and photovoltaic module performance parameters, hourly power generation curves of the east and west photovoltaic arrays under different seasons and weather conditions are simulated, enabling the power generation-price coupling model to more accurately predict power generation behavior. Considering multiple influencing factors, the accuracy and reliability of power generation prediction are improved. The power generation curve and electricity price curve are overlaid in a time series, aiming to maximize net revenue or minimize the cost per kilowatt-hour throughout the project's entire lifecycle. Power allocation strategies and inverter operating parameters are generated through optimization algorithms, ensuring the scientific and economical nature of the decision-making. The application of optimization algorithms such as genetic algorithms, particle swarm optimization, or gradient descent algorithms improves solution efficiency and accuracy, making the power allocation strategy more aligned with actual needs, enhancing overall revenue, and considering long-term economic benefits while avoiding the limitations of short-term decisions.

[0017] Further steps involve using an optimization algorithm to generate initial power allocation strategies and inverter operating parameters for the east and west photovoltaic arrays, aiming to maximize net revenue or minimize the cost per kilowatt-hour throughout the project's lifecycle. These steps include:

[0018] Determine the optimization objective function; wherein, the optimization objective function includes the objective function of maximizing net revenue over the entire life cycle of the project or the objective function of minimizing the cost per kilowatt-hour, the objective function of maximizing net revenue over the entire life cycle of the project is based on maximizing the net value after deducting investment costs and operation and maintenance costs from total power generation revenue, and the objective function of minimizing the cost per kilowatt-hour is based on minimizing the value after dividing the total cost by the total power generation.

[0019] Determine the set of decision variables; wherein the set of decision variables includes the operating parameters of the east inverter and the operating parameters of the west inverter; the operating parameters of the east inverter include the maximum power point tracking voltage range of the east inverter and the output power limit of the east inverter, and the operating parameters of the west inverter include the maximum power point tracking voltage range of the west inverter and the output power limit of the west inverter;

[0020] Set constraints; wherein the constraints include the inverter's maximum power point tracking voltage range constraint and the inverter's output power limit constraint; the inverter's output power limit constraint is set based on the power generation curves simulated by the east and west photovoltaic arrays to ensure that the output power limit does not exceed the maximum possible power generation under given irradiance conditions;

[0021] An optimization algorithm is used to solve the objective function; wherein, the optimization algorithm is used to iteratively optimize the set of decision variables under the premise of satisfying the constraints to obtain the optimal solution; the optimal solution corresponds to a set of power allocation strategies and inverter operating parameters for the east and west photovoltaic arrays; the power allocation strategy is determined by the optimized inverter operating parameters.

[0022] By adopting the above technical solutions, and by determining the objective function of maximizing net revenue or minimizing the cost per kilowatt-hour throughout the project's entire lifecycle, the optimization process focuses more on long-term economic benefits, avoiding revenue losses caused by short-term actions and ensuring the sustainable operation of the photovoltaic power station. The set of decision variables includes the operating parameters of the east and west inverters, such as the maximum power point tracking voltage range and output power limits, ensuring the comprehensiveness and specificity of the control strategy, and enabling fine-tuning for the different characteristics of the east and west arrays. Setting constraints, such as the inverter's maximum power point tracking voltage range constraint and output power limit constraint, ensures that the optimization results are within the physically feasible range, preventing the risk of overload or equipment damage, and improving the system's safety and stability. The optimization algorithm is used to solve the objective function, iteratively optimizing the set of decision variables to obtain the optimal solution, thereby generating an efficient power allocation strategy and inverter operating parameters, improving the system's stability and profitability. At the same time, the optimization process considers actual operating limitations, making the control strategy more practical and reliable.

[0023] Further steps include real-time monitoring of the photovoltaic array's irradiance and power generation, specifically including the following steps:

[0024] The baseline irradiance intensity corresponding to the photovoltaic array on the east and west sides is obtained based on the arrangement of irradiance sensors, wherein the irradiance sensors include at least one photovoltaic radiometer, which is used to measure the total irradiance perpendicular to the surface of the photovoltaic module.

[0025] The corrected irradiance is obtained by multiplying the base irradiance corresponding to the photovoltaic arrays on the east and west sides by a preset irradiance calibration coefficient; wherein, the irradiance calibration coefficient is determined based on the installation tilt angle and azimuth angle of the photovoltaic array, and is used to eliminate measurement deviations caused by sensor installation position and environmental influences;

[0026] The power generation is calculated based on the string power generation obtained from the string-level current and voltage sensors;

[0027] The corrected irradiance and power generation are associated with a timestamp and stored in the monitoring database.

[0028] By adopting the above technical solution, the total irradiance perpendicular to the surface of the photovoltaic module is measured by deploying irradiance sensors such as photovoltaic radiometers, ensuring the accuracy and representativeness of data acquisition and providing high-quality input for subsequent control. Multiplying the base irradiance intensity by the irradiance calibration coefficient yields the corrected irradiance intensity, eliminating measurement deviations caused by sensor installation location and environmental influences, improving data reliability, and reducing control errors due to measurement mistakes. Power generation is acquired based on string-level current and voltage sensors, enabling fine-grained power monitoring, facilitating the identification of local problems such as string faults or shading, and allowing for timely corrective measures. The corrected irradiance intensity and power generation are stored in the monitoring database along with timestamps, providing complete data support for subsequent analysis and control, enhancing system traceability and decision-making foundation, and facilitating long-term performance evaluation and model optimization.

[0029] A further setting involves dynamically adjusting the power output of the east and west photovoltaic arrays based on the generated power allocation strategy and inverter operating parameters, combined with real-time monitoring of irradiance and power generation, to match the power generation curve with peak-valley electricity price periods, thereby maximizing power generation revenue. Specific steps include:

[0030] Based on the power allocation strategy, obtain the target power output values ​​of the east and west photovoltaic arrays corresponding to the current peak-valley electricity price period;

[0031] Based on real-time monitored irradiance, the predicted power generation of the photovoltaic arrays on the east and west sides is obtained through a photovoltaic power generation calculation model. The photovoltaic power generation calculation model calculates the predicted power generation based on the conversion efficiency, temperature coefficient, and real-time irradiance of the photovoltaic modules.

[0032] Using the power output target value as the final control objective, and combining it with the current power generation capacity represented by the predicted power generation, the inverter operating parameters of the east and west photovoltaic arrays are dynamically adjusted through a power adjustment algorithm based on the real-time monitored power generation and the power output target value. The power adjustment algorithm is a proportional-integral-derivative control algorithm or a model predictive control algorithm, used to minimize the power generation deviation and make the power output of the east and west photovoltaic arrays close to the power output target value within a physically feasible range.

[0033] Based on the adjusted inverter operating parameters, the power output of the photovoltaic arrays on the east and west sides is controlled to match the power generation curve with the peak and off-peak electricity price periods, so as to maximize power generation revenue.

[0034] By adopting the above technical solution, the target power output value corresponding to the current peak-valley electricity price period is obtained based on the power allocation strategy, ensuring the consistency between the control target and the electricity price strategy, so that the power generation behavior always revolves around maximizing revenue. The predicted power generation is obtained through the photovoltaic power generation calculation model, which takes into account the conversion efficiency, temperature coefficient and real-time irradiance of the photovoltaic modules, improving the accuracy of the prediction and providing a scientific basis for dynamic adjustment. With the target power output value as the final control target, the inverter operating parameters are dynamically adjusted through power adjustment algorithms such as proportional-integral-derivative control algorithms or model predictive control algorithms, based on the predicted power generation and the real-time monitored power generation, to minimize the power generation deviation and make the power output close to the target value within the physically feasible range, thereby optimizing the power generation curve to match the electricity price period and improving revenue. The adjusted inverter operating parameters directly control the power output, realizing fast response and precise control, enhancing the adaptability and efficiency of the system, and ensuring that optimal performance can be maintained even in a variable environment.

[0035] Further configuration involves using a dynamic string switching method to automatically adjust the string connection status of the east and west photovoltaic arrays based on real-time irradiance changes and shading conditions. Specific steps include:

[0036] Based on a short-term irradiance prediction algorithm, the future power generation capacity of the photovoltaic arrays on the east and west sides is predicted. The short-term irradiance prediction algorithm is based on historical irradiance data, real-time meteorological data, and machine learning models to predict the changes in irradiance intensity within a preset time period. The historical irradiance data comes from local meteorological stations or satellite irradiance databases, and the real-time meteorological data includes cloud cover, temperature, and humidity.

[0037] The system monitors the irradiance and string power generation of the photovoltaic arrays on the east and west sides in real time, and identifies events of significant irradiance reduction or shading based on the irradiance and string power generation. The events of significant irradiance reduction or shading are determined by comparing the real-time irradiance with a preset irradiance threshold or by analyzing abrupt changes in string power generation. The preset irradiance threshold is dynamically adjusted based on the typical irradiance levels and seasonal characteristics of the photovoltaic arrays on the east and west sides.

[0038] When a significant decrease in irradiance or shading event is detected, the affected strings are automatically disconnected. The affected strings are those in the east and west photovoltaic arrays where the power generation of the strings has decreased significantly. The disconnection operation is achieved by controlling the switching devices at the string level. The switching devices at the string level include relays or solid-state switches, which are used to isolate the affected strings to reduce system losses.

[0039] When illumination conditions are restored, the previously disconnected strings are automatically reconnected; wherein, the restoration of illumination conditions is determined by real-time monitoring of irradiance and comparison of real-time irradiance with a preset restoration threshold, and the reconnection operation is achieved by controlling the switching devices at the string level; the preset restoration threshold is set based on the output of the short-term irradiance prediction algorithm and the historical performance data of the east and west photovoltaic arrays.

[0040] Based on the dynamic string switching module, the overall power generation efficiency of the photovoltaic array on the east and west sides is optimized. The dynamic string switching module adjusts the string connection status to enable the photovoltaic array to maintain high-efficiency power generation under the conditions of irradiance variation and shading, and works in conjunction with the power generation-electricity price coupling model to ensure that the power generation curve matches the peak and valley electricity price periods.

[0041] By adopting the above technical solution, the system predicts future power generation capacity based on a short-term irradiance prediction algorithm. It utilizes historical irradiance data, real-time meteorological data, and machine learning models to predict changes in irradiance intensity, improving the system's predictability of future conditions and facilitating advance adjustment of string status to optimize power generation efficiency. Real-time monitoring of irradiance intensity and string power generation identifies events such as significant irradiance drops or shading. By comparing real-time irradiance intensity with preset thresholds or analyzing power fluctuations, the system ensures the timeliness and accuracy of event detection, reducing the risk of misjudgment and missed detection. When an event is detected, the system automatically disconnects the affected strings using string-level switching devices such as relays or fixed-line switches. The dynamic string switching module achieves isolation, reducing system losses, preventing local problems from affecting overall efficiency, and improving system reliability and power generation. When sunlight conditions recover, the strings are automatically reconnected. Based on the comparison between real-time irradiance intensity and preset recovery threshold, it ensures that the system can restore maximum power generation capacity in a timely manner, reducing the need for manual intervention and improving the level of automation. The dynamic string switching module optimizes the overall power generation efficiency, maintains high-efficiency power generation under irradiance changes and shading conditions, and works in conjunction with the power generation-electricity price coupling model to ensure that the power generation curve matches the peak and off-peak electricity price periods, thereby improving revenue and reliability, while enhancing the system's adaptability to complex environments.

[0042] A further step involves periodically updating the power generation-electricity price coupling model to reflect changes in electricity price policies and the performance degradation of photovoltaic arrays, and conducting sensitivity analysis to assess the impact of narrowing peak-valley price differences on power generation revenue. Specific steps include:

[0043] The model update process is triggered periodically; wherein, the periodic triggering is based on a preset update cycle or driven by external events, the preset update cycle is monthly, quarterly or annually, and the external events include electricity price policy release events or photovoltaic array performance test report generation events;

[0044] The system obtains updated electricity pricing policy information and photovoltaic array performance degradation data. The updated electricity pricing policy information is obtained from the local power grid operator or electricity market platform. The photovoltaic array performance degradation data is obtained based on power generation data and photovoltaic module performance parameters from the monitoring database, including module efficiency degradation rate and temperature coefficient changes.

[0045] Based on the updated electricity price policy information and photovoltaic array performance degradation data, the power generation-electricity price coupling model is fundamentally updated to generate a benchmark power allocation strategy and benchmark inverter operating parameters; wherein, the fundamental update includes updating the electricity price curve and photovoltaic module performance parameters in the power generation-electricity price coupling model.

[0046] By adopting the above technical solutions, and by periodically triggering model updates based on a preset update cycle or driven by external events, such as monthly, quarterly, or annual updates, or the release of electricity price policies, the system ensures that the model adapts to changes in a timely manner, maintains relevance, and avoids control deviations caused by outdated data. It acquires updated electricity price policy information and photovoltaic array performance degradation data, obtaining electricity price information from authoritative sources and classifying performance degradation based on monitoring data, ensuring the accuracy and realism of the data and providing reliable input for model updates. The system performs fundamental updates to the model, updating the electricity price curve and photovoltaic module performance parameters, generating a benchmark power allocation strategy and benchmark inverter operating parameters. This ensures that the control strategy is always based on the latest information, optimizes long-term performance, avoids revenue losses due to outdated data, and improves the system's adaptability and sustainability.

[0047] Further steps include periodically updating the power generation-electricity price coupling model to reflect changes in electricity price policies and the performance degradation of photovoltaic arrays, and conducting sensitivity analysis to assess the impact of narrowing peak-valley price differences on power generation revenue. Specifically, this includes the following steps:

[0048] A sensitivity analysis is conducted to assess the impact of the narrowing peak-valley price difference on power generation revenue. The sensitivity analysis involves simulating changes in power generation revenue under different peak-valley price difference scenarios and calculating a revenue sensitivity index, which is the elasticity coefficient of power generation revenue as a function of the peak-valley price difference.

[0049] Based on the revenue sensitivity index obtained from the sensitivity analysis, defensive optimization is performed on the benchmark power allocation strategy and benchmark inverter operating parameters to generate the final applied power allocation strategy and inverter operating parameters. The defensive optimization includes increasing the target power output value of the benchmark power allocation strategy during peak hours or adjusting the inverter output power limit in the benchmark inverter operating parameters to mitigate the negative impact of the narrowing peak-valley price difference on power generation revenue.

[0050] By adopting the above technical solutions, and simulating changes in power generation revenue under different peak-valley price difference scenarios, revenue sensitivity indicators such as elasticity coefficients are calculated, providing a quantitative assessment of risk factors, enhancing the system's risk management capabilities, and facilitating the formulation of response strategies in advance. Based on the sensitivity analysis results, defensive optimizations are performed on the benchmark power allocation strategy and benchmark inverter operating parameters to generate the final application strategies and parameters, such as increasing the target power output value during peak hours or adjusting the inverter output power limit, to mitigate the negative impact of narrowing peak-valley price differences. This ensures that the system can maintain high revenue even under adverse conditions, improves the system's robustness and adaptability, and extends the economic service life of the equipment.

[0051] A further feature is that the east-west asymmetric layout of the photovoltaic array specifically includes:

[0052] The east and west photovoltaic arrays are composed of power generation components, which are either bifacial power generation components or single-sided monocrystalline silicon components.

[0053] By adopting the above technical solutions, bifacial power generation modules can utilize low-angle sunlight and diffused light in the morning to improve overall power generation efficiency, making full use of morning irradiance conditions and increasing power generation, especially during the morning hours when electricity prices may be high, thus improving revenue potential; single-sided monocrystalline silicon modules can resist the impact of high-temperature environments in the afternoon, reducing the negative impact of temperature on power generation efficiency, improving power generation stability in the afternoon, and ensuring that high output can still be maintained under high-temperature conditions.

[0054] A further feature is that the east-west asymmetric layout of the photovoltaic array specifically includes:

[0055] The capacity ratio of the photovoltaic array on the east side ranges from 1.3 to 1.5, while the capacity ratio of the photovoltaic array on the west side ranges from 1.1 to 1.3.

[0056] By adopting the above technical solution, this differentiated capacity ratio design takes into account the different irradiance conditions and component characteristics on the east and west sides. The higher capacity ratio on the east side utilizes the greater irradiance in the morning to maximize morning power generation, while the lower capacity ratio on the west side adapts to the high temperature and irradiance changes in the afternoon.

[0057] In summary, the present invention has the following beneficial effects: by dynamically optimizing photovoltaic power output to accurately match peak and off-peak electricity price periods and adaptively adjusting the string operation status, the overall power generation revenue of photovoltaic power plants is significantly improved. Attached Figure Description

[0058] Figure 1 This is a schematic diagram of the main process of an embodiment;

[0059] Figure 2In this embodiment, based on the local power grid's peak-valley electricity price time period division and electricity price information, combined with the east-west asymmetric layout characteristics of the photovoltaic array, a power generation-electricity price coupling model is established and run to generate a flowchart of the power allocation strategy and inverter operation parameter steps for the east and west photovoltaic arrays.

[0060] Figure 3 This is a flowchart illustrating the steps for real-time monitoring of the irradiance and power generation of the photovoltaic array in this embodiment.

[0061] Figure 4 This is a flowchart illustrating the steps in the embodiment to dynamically adjust the power output of the east and west photovoltaic arrays based on the generated power allocation strategy and inverter operating parameters, combined with real-time monitored irradiance and power generation, so as to match the power generation curve with the peak and valley electricity price period and maximize power generation revenue.

[0062] Figure 5 This is a flowchart illustrating the steps of automatically adjusting the string connection status of the east and west photovoltaic arrays based on real-time irradiance changes and shading conditions using a dynamic string switching method in this embodiment. Detailed Implementation

[0063] The present invention will be further described in detail below with reference to the accompanying drawings.

[0064] As attached Figures 1-5 As shown;

[0065] This embodiment discloses a photovoltaic power output optimization control method that integrates peak-valley electricity price time characteristics, including the following steps:

[0066] S1. Based on the local power grid's peak-valley electricity price period division and electricity price information, and combined with the east-west asymmetric layout characteristics of the photovoltaic array, establish and run a power generation-electricity price coupling model to generate the power allocation strategy and inverter operating parameters of the east-west photovoltaic array; wherein, the peak-valley electricity price period includes peak period, flat period and low period.

[0067] The specific implementation process is as follows: First, the time-of-use pricing policy document is obtained from the public interface or data file of the local power grid operator or electricity market platform. This document is usually stored in a structured format (such as XML, JSON, or CSV) and contains the start and end timestamps of peak, flat, and off-peak periods, as well as their corresponding electricity values. These key parameters are extracted through a parsing module, and a continuous electricity price curve is generated based on the time series. This curve reflects the price fluctuations at different times, providing a basic input for subsequent optimization of power generation revenue. Meanwhile, the system incorporates the asymmetrical east-west layout of the photovoltaic array. The east photovoltaic array uses bifacial power generation modules to utilize low-angle sunlight and diffused light in the morning to improve overall power generation efficiency, while the west photovoltaic array uses monocrystalline silicon modules to resist the impact of high afternoon temperatures. The capacity ratio of the east photovoltaic array (i.e., the ratio of the total capacity of the east photovoltaic array to the rated capacity of the east inverter) ranges from 1.3 to 1.5, and the capacity ratio of the west photovoltaic array (i.e., the ratio of the total capacity of the west photovoltaic array to the rated capacity of the west inverter) ranges from 1.1 to 1.3, to adapt to the irradiance characteristics and temperature tolerance requirements of different orientations.

[0068] Furthermore, the tilt angle of the photovoltaic array on the east side is set within the range of 10°-15°, and the tilt angle of the photovoltaic array on the west side is set within the range of 15°-20°. The specific values ​​of the tilt angles of the east and west photovoltaic arrays are determined by combining local latitude, typical meteorological year data, and roof structure constraints, and by conducting multi-scenario simulations and comparisons using photovoltaic simulation software, with the goal of maximizing power generation revenue. The tilt angle design takes into account structural safety requirements, including wind load and snow load, and its support system meets the requirement of an additional load of not less than 25 kg / m².

[0069] In terms of power generation simulation, a power generation prediction model is constructed based on historical irradiance data and photovoltaic module performance parameters. Historical irradiance data comes from local meteorological stations or satellite irradiance databases, including hourly global horizontal and diffuse irradiance records for many years. Coordinate transformation and tilt surface correction are performed by combining the installation tilt angle, azimuth angle, and geographical coordinates of the photovoltaic array to accurately reflect the actual light-receiving conditions of the east and west photovoltaic arrays. Photovoltaic module performance parameters include module efficiency, temperature coefficient, and degradation rate. These parameters are obtained from the technical specifications provided by the module manufacturer or from on-site measured data and are used to calculate the actual output efficiency of the photovoltaic modules under different ambient temperatures. Simulations are used to model the hourly power generation curves of the east and west photovoltaic arrays under different seasons (e.g., spring, summer, autumn, winter) and weather types (e.g., sunny, cloudy, rainy). The simulation considers factors such as module temperature changes, shading losses, and inverter conversion efficiency to generate high-precision time-series power generation data.

[0070] Then, the simulated power generation curves and electricity price curves of the east and west photovoltaic arrays are overlaid over a time series. With the objective of maximizing net revenue or minimizing the levelized cost of electricity (LCOE) over the project's entire lifecycle, an optimization algorithm is used to generate the initial power allocation strategy and inverter operating parameters for the east and west photovoltaic arrays. Determining the objective function is crucial: if the objective is to maximize net revenue over the project's entire lifecycle, the objective function is based on maximizing the net value after subtracting investment and operation and maintenance costs from total power generation revenue. Total power generation revenue is obtained by integrating the product of power generation and the corresponding electricity price. Investment costs include the initial investment in the photovoltaic array and inverters, and operation and maintenance costs include cleaning, repair, and monitoring expenses. If the objective is to minimize the LCOE, the objective function is based on minimizing the total cost divided by the total power generation. The total cost also includes both investment and operation and maintenance costs. The set of decision variables includes the operating parameters of the east-side inverter and the west-side inverter. The east-side inverter operating parameters include the maximum power point tracking voltage range and the output power limit of the east-side inverter, while the west-side inverter operating parameters include the maximum power point tracking voltage range and the output power limit of the west-side inverter. Constraints are set to ensure system feasibility: the maximum power point tracking voltage range constraint is based on the inverter's technical specifications to prevent the voltage from exceeding the safe operating range; the inverter output power limit constraint is set based on the simulated power generation curves of the east and west photovoltaic arrays to ensure that the output power limit does not exceed the maximum possible power generation under given irradiance conditions, avoiding overload or efficiency loss. The optimization algorithm employs genetic algorithms, particle swarm optimization, or gradient descent algorithms. For example, genetic algorithms iteratively optimize decision variables through selection, crossover, and mutation operations; particle swarm optimization searches for the optimal solution based on swarm intelligence; and gradient descent algorithms utilize the gradient of the objective function for local optimization. Under the premise of satisfying the constraints, the algorithm iteratively optimizes the set of decision variables to obtain the optimal solution. This optimal solution corresponds to a set of power allocation strategies and inverter operating parameters for the east and west photovoltaic arrays. The power allocation strategy is determined by the optimized inverter operating parameters, including the power output allocation scheme of the east and west photovoltaic arrays during peak, flat, and off-peak hours. For example, during peak hours, the output of the east array is prioritized to take advantage of the high electricity price in the morning; during flat hours, the output of the east and west arrays is balanced; and during off-peak hours, the output is reduced to save inverter losses. The inverter operating parameters include the inverter's maximum power point tracking voltage range and output power limit. These parameters are dynamically adjusted by the optimization algorithm to ensure the best match between the power generation curve and the electricity price curve, maximizing power generation revenue or minimizing the cost per kilowatt-hour.

[0071] S2. Real-time monitoring of the irradiance and power generation of the photovoltaic array; wherein the photovoltaic array is composed of multiple strings, the irradiance is obtained through an irradiance sensor, and the power generation is obtained through string-level current and voltage sensors.

[0072] The specific implementation process is as follows: The basic irradiance intensity data is collected in real time by irradiance sensors deployed at key monitoring points of the photovoltaic array on the east and west sides. The irradiance sensors adopt at least one high-precision photovoltaic radiometer that conforms to the IEC 61724 standard. Its spectral response range covers 300-1100nm. The installation position is strictly perpendicular to the surface of the photovoltaic module to accurately measure the total irradiance incident on the module plane. To avoid sensor installation deviations and environmental interference, the system introduces irradiance calibration coefficients to correct the original measurements. First, based on the actual installation tilt angle of the photovoltaic arrays (10°-15° for the east array and 15°-20° for the west array) and azimuth angle (90° for the east array and 270° for the west array), the theoretical irradiance receiving efficiency is calculated. Then, through regression analysis comparing standard radiometer readings with actual power generation data on-site, a dynamically updated irradiance calibration coefficient matrix is ​​generated (irradiance calibration coefficient range for the east array: 0.98-1.02; irradiance calibration coefficient range for the west array: 0.97-1.03). The corrected irradiance intensity calculation formula is as follows: ;in, This indicates the corrected irradiance intensity; This indicates the corresponding baseline irradiance. This indicates the corresponding irradiation calibration factor.

[0073] For power generation monitoring, the system uses string-level high-precision current and voltage sensors (accuracy class 0.5) to collect the output characteristics of each string in real time. The current sensor uses the Hall effect principle to measure DC current in the range of 0-15A, and the voltage sensor uses a resistor divider method to measure DC voltage in the range of 0-1000V. The string power generation is calculated by multiplying the real-time current and voltage, and then undergoes temperature compensation processing (based on PT1000 temperature sensor data to correct for the influence of module temperature). All monitoring data (including corrected irradiance, string power generation, and module backplane temperature) are associated with high-precision timestamps (error ±1ms) generated by Beidou / GPS clocks and transmitted to the edge computing gateway via the Modbus-RTU protocol. After data validity verification (removing outliers and jump points), the data is stored in the time-series database InfluxDB with a storage period of 1 minute per record and a retention period of no less than 3 years. The database is deployed in a distributed architecture, with the master node responsible for real-time data writing and the slave nodes supporting historical data query and analysis interface calls, ensuring the integrity and traceability of the monitoring data.

[0074] S3. Based on the generated power allocation strategy and inverter operating parameters, and combined with the real-time monitored irradiance and power generation, dynamically adjust the power output of the photovoltaic arrays on the east and west sides to match the power generation curve with the peak and valley electricity price period, so as to maximize the power generation revenue.

[0075] The specific implementation process is as follows: First, based on the power allocation strategy, obtain the target power output values ​​for the east and west photovoltaic arrays corresponding to the current peak-valley electricity price period. The power allocation strategy includes the power output allocation schemes for the east and west photovoltaic arrays during peak, flat, and off-peak hours. For example, during peak hours (such as the high electricity price period defined by the local grid, typically 8:00-12:00 AM and 6:00-10:00 PM), the target power output value may be set to more than 90% of the output power limit of the east photovoltaic array to take advantage of the morning high electricity price; during flat hours (such as 12:00-6:00 PM), the target power output value is adjusted to a balanced output between the east and west sides; during off-peak hours (such as 10:00 PM to 8:00 AM the next day), the target power output value is reduced to the minimum operating power to save inverter losses. The system automatically identifies the current peak-valley electricity price period through a real-time clock module and an electricity price strategy database, and queries the corresponding power output target value. These target values ​​are stored in the form of preset power percentages or absolute power values ​​and are dynamically calibrated according to season and weather type. Next, based on the real-time monitored irradiance, the system obtains the predicted power generation of the east and west photovoltaic arrays through a photovoltaic power generation calculation model. The photovoltaic power generation calculation model is based on the conversion efficiency of photovoltaic modules, temperature coefficient, and real-time irradiance. Specifically, the formula for calculating the predicted power generation is: ;in, This indicates the predicted power generation capacity (unit: kilowatts). The corrected irradiance (unit: W / m²) is obtained through an irradiance sensor and corrected by a calibration coefficient; A represents the effective light-receiving area of ​​the photovoltaic array (unit: m²), which is determined based on the installation layout and number of modules of the photovoltaic array on the east and west sides. This indicates the nominal conversion efficiency of a photovoltaic module, based on the module's technical parameters (e.g., the efficiency of the bifacial photovoltaic module on the east side is approximately 21%, and the efficiency of the monocrystalline silicon module on the west side is approximately 19.5%). This represents the temperature coefficient (unit: % / °C), with a typical value of -0.35% / °C. This indicates the real-time temperature of the photovoltaic module (unit: °C), monitored by a backsheet temperature sensor. This represents the reference temperature (typically 25°C). The model runs in real time via an edge computing gateway, updating its forecasts every 5 minutes and continuously optimizing them by incorporating historical irradiance data and weather forecasts to improve prediction accuracy.

[0076] Then, the system uses the power output target value as the final control objective, and combines it with the current power generation capacity represented by the predicted power generation, dynamically adjusting the inverter operating parameters of the east and west photovoltaic arrays based on the real-time monitored power generation and the power output target value through a power adjustment algorithm. The power adjustment algorithm employs a proportional-integral-derivative (PID) control algorithm or a model predictive control algorithm to minimize the power generation deviation and ensure that the power output of the east and west photovoltaic arrays is close to the power output target value within a physically feasible range. In this embodiment, a PID control algorithm is preferred; for example, when using a PID control algorithm, the control error... Defined as real-time monitored power generation With the target power output value The difference between them: Output of the proportional-integral-derivative control algorithm The expression used to adjust the maximum power point tracking voltage range or output power limit of the inverter is: ;in, , and These are the proportional, integral, and differential gain coefficients, determined through on-site commissioning and simulation optimization (e.g., the gain coefficients of the east-side photovoltaic array). Set it to 0.8. Set it to 0.1. Set to 0.05; the western photovoltaic array Set it to 0.7. Set it to 0.15. Set to 0.04) to ensure fast and stable system response. For example, when using the Model Predictive Control (MMCC) algorithm, the MMCC algorithm employs a rolling optimization method based on a linear state-space model and quadratic programming. Specifically, the MMCC algorithm uses the power output of the east and west photovoltaic arrays as the system state and the inverter operating parameters (such as the maximum power point tracking reference value) as the control input. It constructs a quadratic performance index with the objective of maximizing power generation revenue and minimizing power fluctuations over several future sampling periods (e.g., 10 periods in the prediction time domain). In each control period, using the real-time updated irradiance and temperature prediction sequences, a constrained quadratic programming problem is solved online to obtain the optimal inverter control sequence. Only the first step control command in the sequence is sent to the inverter for execution. This optimization process is repeated in the next period, thereby achieving forward-looking dynamic power adjustment. The system uses ultra-short-term prediction data such as irradiance intensity and temperature for a future period (e.g., 15-30 minutes) and peak-valley electricity price information to continuously solve an optimization problem with the objective of maximizing power generation revenue at multiple future moments. The system executes a control cycle every 30 seconds, calculates control quantities in real time and sends them to the inverter controller, and dynamically adjusts the inverter operating parameters. For example, the maximum power point tracking voltage range of the east inverter is adjusted to 450-800V, and the output power limit of the west inverter is adjusted to 85% of the rated power, thereby smoothing power fluctuations and reducing deviations.

[0077] During dynamic adjustment, the system simultaneously considers physical constraints, such as the inverter's maximum power point tracking voltage range constraint (the voltage range of the east-side inverter is limited to 400-820V, and the voltage range of the west-side inverter is limited to 420-800V) and the inverter's output power limit constraint (set based on the power generation curves simulated by the east and west photovoltaic arrays to ensure that the output power does not exceed the maximum possible power generation under given irradiance conditions). For example, when the real-time irradiance intensity drops sharply, the predicted power generation may be lower than the target power output value. The system uses a proportional-integral-derivative control algorithm to reduce the inverter's output power limit to avoid overload; conversely, when irradiance is sufficient, the output is increased to approach the target value.

[0078] Finally, based on the adjusted inverter operating parameters, the system controls the power output of the east and west photovoltaic arrays through the inverter communication interface (such as Modbus-TCP or SunSpec protocol) to match the power generation curve with peak and off-peak electricity price periods. For example, during peak hours, the system prioritizes increasing the output power of the east photovoltaic array, taking advantage of high morning irradiance and high electricity prices; during flat hours, it balances the output of the east and west arrays to maintain network stability; and during off-peak hours, it reduces the output to minimize inverter losses and grid impact. The entire process ensures maximum power generation revenue through real-time data closed-loop feedback. Simultaneously, the system records an adjustment log every 15 minutes, including power deviation, control actions, and revenue indicators, for subsequent performance analysis and model optimization.

[0079] S4. Through a dynamic string switching method, the string connection status of the photovoltaic arrays on the east and west sides is automatically adjusted according to real-time irradiance changes and shading conditions. The dynamic string switching is based on a short-term irradiance prediction algorithm to predict future power generation capacity, and automatically cuts off the affected strings when irradiance drops significantly or shading occurs, and automatically reconnects them after the irradiance conditions are restored.

[0080] The specific implementation process is as follows: In the implementation of the dynamic string switching method, the system first predicts the future power generation capacity of the photovoltaic arrays on the east and west sides based on a short-term irradiance prediction algorithm. This short-term irradiance prediction algorithm integrates historical irradiance data, real-time meteorological data, and a machine learning model. The historical irradiance data comes from local meteorological stations or satellite irradiance databases, including hourly global horizontal irradiance and diffuse irradiance records over many years. Real-time meteorological data is obtained through meteorological monitoring stations deployed on-site, including multi-dimensional parameters such as cloud cover, temperature, humidity, wind speed, and atmospheric pressure. The machine learning model adopts a long short-term memory network architecture. Its input layer contains time-series irradiance data, meteorological variables, and seasonal features. The hidden layer has 128 neurons, and the output layer predicts the irradiance intensity change curve for the next 30 minutes to 2 hours. During model training, historical data for one year is used as the training set, and the root mean square error is used as the loss function. Iterative optimization is performed using the Adam optimizer until the prediction accuracy reaches over 90%. The prediction results are output in the form of minute-by-minute irradiance intensity values ​​and stored in the cache of the edge computing gateway for subsequent power generation capacity assessment and decision support. Real-time monitoring continuously collects data using irradiance sensors and string-level current and voltage sensors deployed at key locations on the east and west sides of the photovoltaic array. The irradiance sensors employ high-precision photovoltaic radiometers, installed precisely perpendicular to the photovoltaic module surface, with a measurement range of 0-1500 W / m² and an accuracy of ±2%. String power generation is acquired in real-time using Hall effect current sensors and resistive voltage divider sensors, with a sampling frequency of 1 Hz. The data undergoes temperature compensation and noise filtering. Significant irradiance reduction events are identified by comparing real-time irradiance intensity with a preset irradiance threshold. This preset threshold is dynamically adjusted based on the typical irradiance levels and seasonal characteristics of the east and west photovoltaic arrays. For example, in clear summer weather, the irradiance threshold for the east photovoltaic array is set at 600 W / m², and for the west array at 550 W / m². A significant irradiance reduction event is identified when the real-time irradiance intensity decreases by more than 30% of the threshold within 5 consecutive minutes. Shading events are identified by analyzing abrupt changes in the power generation of the string: the system calculates the power difference between each string and its neighboring strings. If the difference exceeds 15% and is accompanied by a synchronous decrease in irradiance, the string is marked as an affected string. At the same time, a sliding window mechanism is introduced to perform trend analysis on the power data to distinguish between instantaneous fluctuations and persistent shading.

[0081] When a significant drop in irradiance or shading event is detected, string disconnection is automatically triggered. The determination of affected strings is based on real-time monitoring data of string power generation, verified by the output of a short-term irradiance prediction algorithm, ensuring that only truly affected strings are isolated. The disconnection operation is implemented by controlling string-level switching devices, including high-reliability relays or solid-state switches with a rated current of 15A, a rated voltage of 1000V DC, and a switching time of less than 100 milliseconds. The system sends disconnection commands to the switch controller via Modbus-RTU or CAN bus protocols, including string identifiers and action codes. During disconnection, the system records string status change logs, including timestamps, string numbers, disconnection reasons, and operation results, and updates the photovoltaic array topology diagram in real time. After disconnection, the affected strings are isolated from the main circuit to prevent them from lowering the overall array voltage and increasing system losses. Simultaneously, the system adjusts the maximum power point tracking parameters via the inverter communication interface to adapt to the new array configuration.

[0082] When illumination conditions recover, the system automatically reconnects the previously disconnected strings. The determination of restored illumination conditions is based on a comparison between real-time monitored irradiance and a preset recovery threshold. This preset recovery threshold is jointly set by the output of the short-term irradiance prediction algorithm and historical performance data of the east and west photovoltaic arrays. For example, when the real-time irradiance remains stable above the threshold for 10 consecutive minutes (the recovery threshold for the east photovoltaic array is set at 500 W / m², and for the west photovoltaic array at 480 W / m²) and no significant downward trend is predicted within the next 30 minutes, a reconnection operation is triggered. The reconnection process is executed through the same string-level switching devices. The system first performs a pre-check on the string to be reconnected, including checking whether its open-circuit voltage and short-circuit current are within the normal range to avoid reverse current or surge risks. After the pre-check passes, the controller sends a reconnection command, the switch closes, and the string is reconnected to the circuit. The system synchronously updates the routing status and resumes power acquisition for that string. The reconnection operation adopts a soft-start strategy, gradually increasing the inverter's power absorption to avoid current surges and ensure a smooth system transition.

[0083] A dynamic string switching module optimizes the overall power generation efficiency of the east and west photovoltaic (PV) arrays. This module adjusts string connection status in real time to maintain high-efficiency power generation under varying irradiance and shading conditions. Its core logic is to maximize the output of available strings and minimize mismatch losses. Switching decisions are coordinated with a power generation-price coupling model to ensure the power generation curve matches peak and off-peak electricity price periods. For example, during peak hours, the system prioritizes maintaining full string connectivity on the east side of the PV array to take advantage of high prices, even if some strings are slightly shaded; during off-peak or low-peak hours, switching is strictly based on efficiency principles. The system performs a global optimization calculation every 5 minutes, re-evaluating the string switching scheme based on short-term irradiance predictions, real-time load, and price strategies, and disseminating decision commands to field equipment via an anti-interference communication protocol. This entire process enables intelligent self-healing and efficiency improvement for the PV array, while generating performance reports at the edge gateway, including switching counts, power generation gain, and revenue impact indicators, for subsequent model calibration and operation and maintenance analysis.

[0084] S5. Regularly update the power generation-electricity price coupling model to reflect changes in electricity price policies and the performance degradation of photovoltaic arrays, and conduct sensitivity analysis to assess the impact of the narrowing peak-valley price difference on power generation revenue.

[0085] The specific implementation process is as follows: The model update process is triggered by a preset update cycle or an external event-driven mechanism. The preset update cycle can be configured to perform a full update monthly, quarterly, or annually. External events include the release of new time-of-use pricing policy documents by the local power grid or the generation of photovoltaic array performance test reports by the operation and maintenance platform. When the triggering conditions are met, the system first obtains the latest electricity price policy information from the open data interface of the local power grid operator or the electricity market platform. This information is transmitted in a structured format (such as JSON or XML), including the start and end times of peak, flat, and off-peak periods and their corresponding electricity values. The system then extracts key parameters through the data parsing module to regenerate the electricity price curve. Simultaneously, based on historical power generation data stored in the monitoring database (such as time-series data collected by string-level current and voltage sensors) and on-site photovoltaic module test reports, the system analyzes the photovoltaic array performance degradation data, including the annual degradation rate of module efficiency (usually 100%). The temperature coefficient drift and the change in back panel transmittance were calculated by comparing the parameters with the initial performance parameters using a linear regression model.

[0086] After obtaining updated electricity price policy information and photovoltaic array performance degradation data, the system performs a basic update to the power generation-electricity price coupling model: First, the electricity price curve in the model is replaced with the latest version, and the photovoltaic module performance parameters are corrected based on the degradation data. For example, the efficiency of the bifacial power generation module on the east side is adjusted from the initial 21% to 20.6%, and the efficiency of the monocrystalline silicon module on the west side is adjusted from 19.5% to 19.1%. Then, the power generation simulation module is rerun, and the updated hourly power generation curves of the east and west photovoltaic arrays are generated by combining historical irradiance data. The model uses a lifecycle cost-benefit algorithm to recalculate the optimal solution, generating a baseline power allocation strategy and baseline inverter operating parameters. The baseline power allocation strategy includes adjusting the output ratio of the east and west photovoltaic arrays to 85% of the east array and 75% of the west array during peak hours, 70% of the east array and 65% of the west array during off-peak hours, and 30% of the east array and 25% of the west array during off-peak hours. The baseline inverter operating parameters include updating the maximum power point tracking voltage range of the east inverter to 440-810V and setting the output power limit to 88% of the rated capacity, and adjusting the voltage range of the west inverter to 430-790V and setting the output power limit to 82% of the rated capacity.

[0087] The system then performs sensitivity analysis to assess the impact of narrowing peak-valley price differences on power generation revenue. By simulating different price difference narrowing scenarios (e.g., peak electricity prices decreasing by 10%-30% while off-peak electricity prices increase by 5%-15%), the system calculates the change in power generation revenue under each scenario and uses an elasticity coefficient algorithm to determine the revenue sensitivity index, defined as the ratio of the rate of change in power generation revenue to the rate of change in the peak-valley price difference. Specifically, the system constructs a multi-dimensional sensitivity analysis matrix. The matrix inputs include the price difference narrowing range, irradiance condition type, and load characteristics. The outputs are the revenue sensitivity curve and the critical price difference threshold (e.g., a significant decrease in revenue when the price difference narrows to 0.35 yuan / kWh). Based on the sensitivity analysis results, the system implements defensive optimizations to the benchmark power allocation strategy and benchmark inverter operating parameters: For scenarios with narrowing price differences, the target power output during peak hours is increased (e.g., the output of the east-side photovoltaic array is increased to 90%, and the output of the west-side photovoltaic array is increased to 80%), and the output power limits in the inverter operating parameters are adjusted (the limit for the east-side inverter is increased to 90%, and the limit for the west-side inverter is increased to 85%) to maximize peak-hour revenue; simultaneously, the power allocation ratio during off-peak hours is optimized, increasing the output weight of the east-side array to utilize its morning power generation advantage. The final generated power allocation strategy and inverter operating parameters are deployed to the field controller through the strategy distribution module, and optimization logs are recorded for subsequent iterative optimization.

[0088] This specific embodiment is merely an explanation of the present invention and is not intended to limit the invention. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but as long as they are within the scope of the claims of the present invention, they are protected by law.

Claims

1. A photovoltaic power output optimization control method integrating peak-valley electricity price time characteristics, characterized in that, Includes the following steps: Based on the local power grid's peak-valley electricity price period division and price information, and combined with the east-west asymmetric layout characteristics of the photovoltaic array, a power generation-electricity price coupling model is established and run to generate power allocation strategies and inverter operating parameters for the east and west photovoltaic arrays. The peak-valley electricity price periods include peak periods, flat periods, and low periods. The capacity ratio of the east photovoltaic array ranges from 1.3 to 1.5, and the capacity ratio of the west photovoltaic array ranges from 1.1 to 1.

3. The irradiance and power generation of the photovoltaic array are monitored in real time; wherein the photovoltaic array is composed of multiple strings, the irradiance is obtained through an irradiance sensor, and the power generation is obtained through string-level current and voltage sensors; Based on the generated power allocation strategy and inverter operating parameters, and combined with real-time monitoring of irradiance and power generation, the power output of the photovoltaic arrays on the east and west sides is dynamically adjusted to match the power generation curve with the peak and valley electricity price period, so as to maximize power generation revenue. The dynamic string switching method automatically adjusts the string connection status of the photovoltaic arrays on the east and west sides based on real-time irradiance changes and shading conditions. The dynamic string switching is based on a short-term irradiance prediction algorithm to predict future power generation capacity, and automatically cuts off the affected strings when irradiance drops significantly or shading occurs, and automatically reconnects them after the light conditions are restored. The power generation-electricity price coupling model is updated regularly to reflect changes in electricity price policies and the performance degradation of photovoltaic arrays, and sensitivity analysis is conducted to assess the impact of the narrowing peak-valley price difference on power generation revenue.

2. The photovoltaic power output optimization control method based on peak-valley electricity price time characteristics according to claim 1, characterized in that, Based on the local power grid's peak-valley electricity price time period division and price information, and combined with the east-west asymmetric layout characteristics of the photovoltaic array, a power generation-electricity price coupling model is established and run to generate the power allocation strategy and inverter operating parameters for the east and west photovoltaic arrays. Specific steps include: Obtain time-of-use pricing policy documents from local power grid operators or electricity market platforms, analyze and extract the start and end times and corresponding electricity values ​​for peak, flat, and off-peak periods, and generate electricity price curves; Based on historical irradiance data and photovoltaic module performance parameters, the hourly power generation curves of the photovoltaic arrays on the east and west sides under different seasons and weather conditions are simulated; wherein, the historical irradiance data comes from local meteorological stations or satellite irradiance databases, and the photovoltaic module performance parameters include module efficiency, temperature coefficient and degradation rate; The power generation curves simulated by the east and west photovoltaic arrays are overlaid with the electricity price curves over time. With the goal of maximizing net revenue or minimizing the cost per kilowatt-hour over the project's entire lifecycle, an optimization algorithm is used to generate initial power allocation strategies and inverter operating parameters for the east and west photovoltaic arrays. The power allocation strategy includes power output distribution schemes for the east and west photovoltaic arrays during peak, off-peak, and low-peak hours. The inverter operating parameters include the inverter's maximum power point tracking voltage range and output power limit. The optimization algorithm includes genetic algorithms, particle swarm optimization, or gradient descent algorithms.

3. The photovoltaic power output optimization control method based on peak-valley electricity price time characteristics according to claim 2, characterized in that, With the goal of maximizing net revenue or minimizing cost per kilowatt-hour over the entire project lifecycle, the initial power allocation strategy and inverter operating parameters for the east and west photovoltaic arrays are generated through optimization algorithms. Specific steps include: Determine the optimization objective function; wherein, the optimization objective function includes the objective function of maximizing net revenue over the entire life cycle of the project or the objective function of minimizing the cost per kilowatt-hour, the objective function of maximizing net revenue over the entire life cycle of the project is based on maximizing the net value after deducting investment costs and operation and maintenance costs from total power generation revenue, and the objective function of minimizing the cost per kilowatt-hour is based on minimizing the value after dividing the total cost by the total power generation. Determine the set of decision variables; wherein the set of decision variables includes the operating parameters of the east inverter and the operating parameters of the west inverter; the operating parameters of the east inverter include the maximum power point tracking voltage range of the east inverter and the output power limit of the east inverter, and the operating parameters of the west inverter include the maximum power point tracking voltage range of the west inverter and the output power limit of the west inverter; Set constraints; wherein the constraints include the inverter's maximum power point tracking voltage range constraint and the inverter's output power limit constraint; the inverter's output power limit constraint is set based on the power generation curves simulated by the east and west photovoltaic arrays to ensure that the output power limit does not exceed the maximum possible power generation under given irradiance conditions; An optimization algorithm is used to solve the objective function; wherein, the optimization algorithm is used to iteratively optimize the set of decision variables under the premise of satisfying the constraints to obtain the optimal solution; the optimal solution corresponds to a set of power allocation strategies and inverter operating parameters for the east and west photovoltaic arrays; the power allocation strategy is determined by the optimized inverter operating parameters.

4. The photovoltaic power output optimization control method based on peak-valley electricity price time characteristics according to claim 1, characterized in that, Real-time monitoring of the irradiance and power generation of a photovoltaic array includes the following steps: The baseline irradiance intensity corresponding to the photovoltaic array on the east and west sides is obtained based on the arrangement of irradiance sensors, wherein the irradiance sensors include at least one photovoltaic radiometer, which is used to measure the total irradiance perpendicular to the surface of the photovoltaic module. The corrected irradiance is obtained by multiplying the base irradiance corresponding to the photovoltaic arrays on the east and west sides by a preset irradiance calibration coefficient; wherein, the irradiance calibration coefficient is determined based on the installation tilt angle and azimuth angle of the photovoltaic array, and is used to eliminate measurement deviations caused by sensor installation position and environmental influences; The power generation is calculated based on the string power generation obtained from the string-level current and voltage sensors; The corrected irradiance and power generation are associated with a timestamp and stored in the monitoring database.

5. The photovoltaic power output optimization control method based on peak-valley electricity price time characteristics according to claim 1, characterized in that, Based on the generated power allocation strategy and inverter operating parameters, and combined with real-time monitored irradiance and power generation, the power output of the east and west photovoltaic arrays is dynamically adjusted to match the power generation curve with peak and off-peak electricity price periods, in order to maximize power generation revenue. Specific steps include: Based on the power allocation strategy, obtain the target power output values ​​of the east and west photovoltaic arrays corresponding to the current peak-valley electricity price period; Based on real-time monitored irradiance, the predicted power generation of the photovoltaic arrays on the east and west sides is obtained through a photovoltaic power generation calculation model. The photovoltaic power generation calculation model calculates the predicted power generation based on the conversion efficiency, temperature coefficient, and real-time irradiance of the photovoltaic modules. Using the power output target value as the final control objective, and combining it with the current power generation capacity represented by the predicted power generation, the inverter operating parameters of the east and west photovoltaic arrays are dynamically adjusted through a power adjustment algorithm based on the real-time monitored power generation and the power output target value. The power adjustment algorithm is a proportional-integral-derivative control algorithm or a model predictive control algorithm, used to minimize the power generation deviation and make the power output of the east and west photovoltaic arrays close to the power output target value within a physically feasible range. Based on the adjusted inverter operating parameters, the power output of the photovoltaic arrays on the east and west sides is controlled to match the power generation curve with the peak and off-peak electricity price periods, so as to maximize power generation revenue.

6. The photovoltaic power output optimization control method based on peak-valley electricity price time characteristics according to claim 1, characterized in that, The dynamic string switching method automatically adjusts the string connection status of the east and west photovoltaic arrays based on real-time irradiance changes and shading conditions. Specific steps include: Based on a short-term irradiance prediction algorithm, the future power generation capacity of the photovoltaic arrays on the east and west sides is predicted. The short-term irradiance prediction algorithm is based on historical irradiance data, real-time meteorological data, and machine learning models to predict the changes in irradiance intensity within a preset time period. The historical irradiance data comes from local meteorological stations or satellite irradiance databases, and the real-time meteorological data includes cloud cover, temperature, and humidity. The system monitors the irradiance and string power generation of the photovoltaic arrays on the east and west sides in real time, and identifies events of significant irradiance reduction or shading based on the irradiance and string power generation. The events of significant irradiance reduction or shading are determined by comparing the real-time irradiance with a preset irradiance threshold or by analyzing abrupt changes in string power generation. The preset irradiance threshold is dynamically adjusted based on the typical irradiance levels and seasonal characteristics of the photovoltaic arrays on the east and west sides. When a significant decrease in irradiance or shading event is detected, the affected strings are automatically disconnected. The affected strings are those in the east and west photovoltaic arrays where the power generation of the strings has decreased significantly. The disconnection operation is achieved by controlling the switching devices at the string level. The switching devices at the string level include relays or solid-state switches, which are used to isolate the affected strings to reduce system losses. When illumination conditions are restored, the previously disconnected strings are automatically reconnected; wherein, the restoration of illumination conditions is determined by real-time monitoring of irradiance and comparison of real-time irradiance with a preset restoration threshold, and the reconnection operation is achieved by controlling the switching devices at the string level; the preset restoration threshold is set based on the output of the short-term irradiance prediction algorithm and the historical performance data of the east and west photovoltaic arrays. Based on the dynamic string switching module, the overall power generation efficiency of the photovoltaic array on the east and west sides is optimized. The dynamic string switching module adjusts the string connection status to enable the photovoltaic array to maintain high-efficiency power generation under the conditions of irradiance variation and shading, and works in conjunction with the power generation-electricity price coupling model to ensure that the power generation curve matches the peak and valley electricity price periods.

7. The photovoltaic power output optimization control method based on peak-valley electricity price time characteristics according to claim 1, characterized in that, The generation-price coupling model is updated regularly to reflect changes in electricity pricing policies and the performance degradation of photovoltaic arrays. Sensitivity analysis is conducted to assess the impact of narrowing peak-valley price differences on generation revenue. Specific steps include: The model update process is triggered periodically; wherein, the periodic triggering is based on a preset update cycle or driven by external events, the preset update cycle is monthly, quarterly or annually, and the external events include electricity price policy release events or photovoltaic array performance test report generation events; The system obtains updated electricity pricing policy information and photovoltaic array performance degradation data. The updated electricity pricing policy information is obtained from the local power grid operator or electricity market platform. The photovoltaic array performance degradation data is obtained based on power generation data and photovoltaic module performance parameters from the monitoring database, including module efficiency degradation rate and temperature coefficient changes. Based on the updated electricity price policy information and photovoltaic array performance degradation data, the power generation-electricity price coupling model is fundamentally updated to generate a benchmark power allocation strategy and benchmark inverter operating parameters; wherein, the fundamental update includes updating the electricity price curve and photovoltaic module performance parameters in the power generation-electricity price coupling model.

8. The photovoltaic power output optimization control method based on peak-valley electricity price time characteristics according to claim 7, characterized in that, The power generation-electricity price coupling model is updated regularly to reflect changes in electricity price policies and the performance degradation of photovoltaic arrays. Sensitivity analysis is also conducted to assess the impact of the narrowing peak-valley price gap on power generation revenue. Specifically, this includes the following steps: A sensitivity analysis is conducted to assess the impact of the narrowing peak-valley price difference on power generation revenue. The sensitivity analysis involves simulating changes in power generation revenue under different peak-valley price difference scenarios and calculating a revenue sensitivity index, which is the elasticity coefficient of power generation revenue as a function of the peak-valley price difference. Based on the revenue sensitivity index obtained from the sensitivity analysis, defensive optimization is performed on the benchmark power allocation strategy and benchmark inverter operating parameters to generate the final applied power allocation strategy and inverter operating parameters. The defensive optimization includes increasing the target power output value of the benchmark power allocation strategy during peak hours or adjusting the inverter output power limit in the benchmark inverter operating parameters to mitigate the negative impact of the narrowing peak-valley price difference on power generation revenue.

9. The photovoltaic power output optimization control method based on peak-valley electricity price time characteristics according to claim 1, characterized in that, The east-west asymmetric layout features of the photovoltaic array specifically include: The east and west photovoltaic arrays are composed of power generation components, which are either bifacial power generation components or single-sided monocrystalline silicon components.

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