Photovoltaic power output optimization control method fusing peak-valley electricity price time period characteristics

Abstract

The invention discloses a photovoltaic power output optimization control method fusing peak-valley electricity price time period characteristics, and the method comprises the following steps: building and operating a generating capacity-electricity price coupling model, and generating a power distribution strategy of an east-west side photovoltaic array and inverter operation parameters; monitoring the irradiation intensity and the generated power of the photovoltaic array in real time; dynamically adjusting the power output of the east-side photovoltaic array and the west-side photovoltaic array, and enabling a power generation curve to be matched with a peak-valley electricity price period, so as to maximize the power generation income; through a dynamic string switching method, string connection states of east and west photovoltaic arrays are automatically adjusted according to real-time irradiation changes and shadow shielding conditions; and the generating capacity-electricity price coupling model is updated regularly. The method has the following advantages and effects: through dynamic optimization of photovoltaic power output, peak-valley electricity price time periods are accurately matched, the string operation state is adaptively adjusted, and the overall power generation income of a photovoltaic power station is significantly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of photovoltaic power generation, in particular to a photovoltaic power output optimization control method fusing peak-valley electricity price period characteristics. BACKGROUND

[0002] With the rapid development of photovoltaic power generation technology and the widespread application of time-of-use electricity price policy, how to maximize the benefits of photovoltaic power generation has become a key problem for power station operation. The time-of-use electricity price policy divides a day into different electricity price periods such as peak period, flat period and valley period, among which the electricity price in the peak period is higher and the electricity price in the valley period is lower. The output power of photovoltaic power generation is affected by factors such as irradiance, weather conditions and array layout, and has significant intermittency and volatility, resulting in a mismatch between the power generation curve and the electricity price curve. For example, in an east-west asymmetrically laid photovoltaic array, the east array generates more power in the morning and the west array generates more power in the afternoon, but the peak electricity price period may occur in the period of high morning and evening electricity demand, so that the photovoltaic power generation is insufficient in the high price period and excessive in the low price period, thereby reducing the overall power generation benefit. The existing photovoltaic power control methods mostly focus on maximum power point tracking or local shadow optimization, but lack deep integration with the time-of-use electricity price policy, and cannot dynamically adjust the power output to match the electricity price period. In addition, the traditional method is difficult to cope with real-time irradiance changes and shadow shading, resulting in low power generation efficiency and benefit loss. Therefore, in the time-of-use electricity price environment, how to match the photovoltaic power generation curve with the peak-valley electricity price period through optimization control has become a core problem to improve power generation benefit. SUMMARY

[0003] The purpose of the present application is to provide a photovoltaic power output optimization control method fusing peak-valley electricity price period characteristics to solve the problems proposed in the background art.

[0004] The above technical purpose of the present application is realized by the following technical scheme: A photovoltaic power output optimization control method fusing peak-valley electricity price period characteristics, comprising the following steps: Based on the peak-valley electricity price period division and electricity price information of the local power grid, combined with the east-west asymmetric layout characteristics of the photovoltaic array, a power generation-price coupling model is established and run to generate power distribution strategies and inverter running parameters for the east and west photovoltaic arrays; wherein the peak-valley electricity price period includes peak period, flat period and valley period; Real-time monitoring of the irradiance and power generation power of the photovoltaic array; wherein the photovoltaic array is composed of multiple groups of strings, the irradiance is obtained through a radiation sensor, and the power generation power is obtained through a group string level current-voltage sensor; Based on the generated power distribution strategy and inverter operation parameters, combined with real-time monitoring of irradiance and power generation, the power output of the east-west photovoltaic array is dynamically adjusted to make the power generation curve match the peak and valley electricity price period, maximizing power generation revenue; By the dynamic string switching method, the connection state of the east-west photovoltaic array is automatically adjusted according to real-time irradiance changes and shadow shielding. The dynamic string switching is based on short-term irradiance prediction algorithm to predict future power generation capacity, and automatically removes the affected string when irradiance significantly decreases or shadow shielding occurs, and automatically reconnects when the light conditions recover. The power generation-price coupling model is updated regularly to reflect changes in electricity price policy and photovoltaic array performance degradation, and sensitivity analysis is performed to evaluate the impact of narrowing peak-valley price difference on power generation revenue.

[0005] By using the above technical solutions, by establishing and running the power generation-price coupling model, the power distribution strategy and inverter operation parameters of the east-west photovoltaic array can be generated according to the local power grid peak-valley electricity price period division and electricity price information, combined with the east-west asymmetric layout characteristics of the photovoltaic array, so that the photovoltaic power generation system can actively adapt to the change of electricity price, increase the power output as much as possible in the peak period, and reasonably control the power generation in the valley period, avoiding resource waste and significantly improving the power generation revenue. Real-time monitoring of the irradiance and power generation of the photovoltaic array, through the irradiance sensor and string-level current and voltage sensor, accurate environmental and operating data are obtained to provide reliable basis for dynamic control, ensuring timely and accurate system response. Based on the generated power distribution strategy and inverter operation parameters, combined with real-time monitoring data, the power output of the east-west photovoltaic array is dynamically adjusted to make the power generation curve highly match the peak and valley electricity price period, maximizing power generation revenue, solving the problem of disconnection between power generation and electricity price in traditional methods. By the dynamic string switching method, the connection state of the east-west photovoltaic array is automatically adjusted according to real-time irradiance changes and shadow shielding. Based on the short-term irradiance prediction algorithm, the future power generation capacity is predicted, and the affected string is automatically removed when the irradiance significantly decreases or shadow shielding occurs, and automatically reconnects when the light conditions recover, effectively reducing the impact of local shadow or irradiance fluctuation on overall power generation efficiency, improving the stability and efficiency of the system. The power generation-price coupling model is updated regularly to reflect changes in electricity price policy and photovoltaic array performance degradation, and sensitivity analysis is performed to evaluate the impact of narrowing peak-valley price difference on power generation revenue, ensuring that the system can maintain an optimized state for a long time, adapt to external environmental changes, maintain a high revenue level, and prolong the economic life of the photovoltaic power station.

[0006] Further settings are, based on the peak and valley period division and electricity price information of the local power grid, combined with the east-west asymmetric layout characteristics of the photovoltaic array, a power generation-price coupling model is established and run to generate the power distribution strategy and inverter operation parameter of the east-west photovoltaic array, including the following steps: Obtain the time-of-use electricity price policy file from the local power grid operator or power market platform, parse and extract the start and end times of the peak period, flat period and valley period, and the corresponding electricity value, and generate an electricity price curve; Based on the historical irradiation data and photovoltaic component performance parameters, simulate the hourly power generation curve of the east-west photovoltaic array under different seasons and weather conditions; wherein the historical irradiation data comes from the local weather station or satellite irradiation database, and the photovoltaic component performance parameters include component efficiency, temperature coefficient and attenuation rate; Superimpose the simulated power generation curve of the east-west photovoltaic array and the electricity price curve in time sequence, take the maximum net income or the minimum cost per kilowatt-hour in the whole life cycle of the project as the target, and generate the initial power distribution strategy and inverter operation parameter of the east-west photovoltaic array through an optimization algorithm; wherein the power distribution strategy includes the power output distribution scheme of the east-west photovoltaic array in the peak period, flat period and valley period, and the inverter operation parameter includes the maximum power point tracking voltage range and output power limit value of the inverter; wherein the optimization algorithm includes genetic algorithm, particle swarm optimization algorithm or gradient descent algorithm.

[0007] By adopting the above technical scheme, by obtaining the time-of-use electricity price policy file from the local power grid operator or power market platform, parsing and extracting the start and end times of the peak period, flat period and valley period, and the corresponding electricity value, and generating an electricity price curve, the accuracy and timeliness of the electricity price data are ensured, providing a reliable foundation for subsequent optimization; based on the historical irradiation data and photovoltaic component performance parameters, the hourly power generation curve of the east-west photovoltaic array under different seasons and weather conditions is simulated, so that the power generation-price coupling model can more accurately predict the power generation behavior, consider various influencing factors, and improve the accuracy and reliability of power generation prediction; superimpose the power generation curve and the electricity price curve in time sequence, take the maximum net income or the minimum cost per kilowatt-hour in the whole life cycle of the project as the target, and generate the power distribution strategy and inverter operation parameter through the optimization algorithm, ensure the scientificity and economy of the decision, and the application of optimization algorithms such as genetic algorithm, particle swarm optimization algorithm or gradient descent algorithm improves the solving efficiency and accuracy, so that the power distribution strategy is more in line with the actual demand, improves the overall income, and at the same time considers the long-term economic benefit, avoiding the limitations of short-term decision-making.

[0008] Further settings are, take the maximum net income or the minimum cost per kilowatt-hour in the whole life cycle of the project as the target, and generate the initial power distribution strategy and inverter operation parameter of the east-west photovoltaic array through an optimization algorithm, including the following steps: determining an optimization objective function; wherein the optimization objective function comprises a maximum net benefit objective function or a minimum cost per kilowatt-hour objective function in the whole life cycle of the project, the maximum net benefit objective function in the whole life cycle of the project is to maximize the net value based on the total power generation income minus the investment cost and the operation and maintenance cost, and the minimum cost per kilowatt-hour objective function is to minimize the value based on the total power generation divided by the total cost; determining a decision variable set; wherein the decision variable set comprises east side inverter operating parameters and west side inverter operating parameters; the east side inverter operating parameters comprise east side inverter maximum power point tracking voltage range and east side inverter output power limit, and the west side inverter operating parameters comprise west side inverter maximum power point tracking voltage range and west side inverter output power limit; setting a constraint condition; wherein the constraint condition comprises inverter maximum power point tracking voltage range constraint and inverter output power limit constraint; the inverter output power limit constraint is set based on the simulated power generation power curve of the east and west side photovoltaic arrays to ensure that the output power limit does not exceed the maximum possible power generation power under given irradiation conditions; solving the optimization objective function by using an optimization algorithm; wherein the optimization algorithm is used to iteratively optimize the decision variable set under the premise of meeting the constraint condition to obtain an optimal solution; the optimal solution corresponds to a set of power distribution strategies and inverter operating parameters of the east and west side photovoltaic arrays; the power distribution strategy is determined by the optimized inverter operating parameters.

[0009] By using the above technical solutions, the maximum net benefit objective function or the minimum cost per kilowatt-hour objective function in the whole life cycle of the project is determined, so that the optimization process pays more attention to long-term economic benefits, avoids the loss of benefits caused by short-term behavior, and ensures the sustainable operation of the photovoltaic power station; the decision variable set comprises east and west side inverter operating parameters such as maximum power point tracking voltage range and output power limit, which ensures the comprehensiveness and pertinence of the control strategy and can finely adjust the different characteristics of the east and west side arrays; the constraint condition such as inverter maximum power point tracking voltage range constraint and output power limit constraint is set to ensure that the optimization result is within the physically feasible range, prevent the risk of overload or equipment damage, and improve the safety and stability of the system; the optimization algorithm is used to solve the objective function, iteratively optimize the decision variable set, and obtain the optimal solution, so as to generate efficient power distribution strategies and inverter operating parameters, improve the stability and benefit capacity of the system, and at the same time, the optimization process considers the actual operation limit, so that the control strategy is more practical and reliable.

[0010] Further, the real-time monitoring of the irradiance and power generation of the photovoltaic array comprises the following steps: acquire the basic irradiance intensity corresponding to the east-west photovoltaic array based on arranged irradiation sensors, the irradiation sensors including at least one photovoltaic radiometer for measuring the total irradiance perpendicular to the surface of the photovoltaic module; multiply the basic irradiance intensity corresponding to the east-west photovoltaic array by a preset irradiation calibration coefficient to obtain the corrected irradiance intensity; wherein the irradiation calibration coefficient is determined based on the installation inclination and azimuth angle of the photovoltaic array, and is used to eliminate the measurement deviation caused by the installation position and environment of the sensor; the power generation power is calculated based on the power generation power of the string acquired by the current-voltage sensor of the string level; store the corrected irradiance intensity and the power generation power in association with the time stamp to the monitoring database.

[0011] By adopting the above technical solution, the total irradiance perpendicular to the surface of the photovoltaic module is measured by arranging the irradiation sensors such as photovoltaic radiometers, which ensures the accuracy and representativeness of data acquisition and provides high-quality input for subsequent control; the basic irradiance intensity is multiplied by the irradiation calibration coefficient to obtain the corrected irradiance intensity, which eliminates the measurement deviation caused by the installation position and environment of the sensor, improves the reliability of the data, and reduces the control failure caused by measurement error; the power generation power is acquired based on the current-voltage sensor of the string level, which realizes fine-grained power monitoring and facilitates the identification of local problems such as string failure or shadow shielding, so that corrective measures can be taken in time; the corrected irradiance intensity and the power generation power are stored in association with the time stamp to the monitoring database, which provides complete data support for subsequent analysis and control, enhances the traceability and decision basis of the system, and facilitates long-term performance evaluation and model optimization.

[0012] Further, based on the generated power distribution strategy and inverter operation parameters, and in combination with the real-time monitored irradiance intensity and power generation power, the power output of the east-west photovoltaic array is dynamically adjusted to match the power generation curve with the peak-valley electricity price period, so as to maximize the power generation benefit, which specifically includes the following steps: based on the power distribution strategy, acquire the power output target value of the east-west photovoltaic array corresponding to the current belonging peak-valley electricity price period; based on the real-time monitored irradiance intensity, obtain the predicted power generation power of the east-west photovoltaic array through a photovoltaic power generation power calculation model; wherein the photovoltaic power generation power calculation model calculates the predicted power generation power based on the conversion efficiency, temperature coefficient and real-time irradiance intensity of the photovoltaic module; take the power output target value as the final control target, and combine the current power generation capacity represented by the predicted power generation, to dynamically adjust the inverter operating parameters of the east-west side photovoltaic array through a power adjustment algorithm based on the real-time monitored power generation and the power output target value; wherein 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-west side photovoltaic array close to the power output target value within a physically feasible range; Based on the adjusted inverter operating parameters, control the power output of the east-west side photovoltaic array to match the power generation curve with the peak-valley electricity price period to maximize power generation revenue.

[0013] By adopting the above technical solution, the power output target value corresponding to the current peak-valley electricity price period is obtained based on the power distribution strategy, ensuring the consistency of the control target and the electricity price strategy, so that the power generation behavior always focuses on maximizing revenue; the predicted power generation is obtained through a photovoltaic power generation power calculation model, which takes into account the conversion efficiency, temperature coefficient and real-time irradiance of the photovoltaic components, improving the accuracy of the prediction and providing a scientific basis for dynamic adjustment; taking the power output target value as the final control target, combining the predicted power generation and the real-time monitored power generation, and dynamically adjusting the inverter operating parameters through a power adjustment algorithm such as a proportional-integral-derivative control algorithm or a model predictive control algorithm minimizes the power generation deviation and makes the power output close to the target value within a physically feasible range, thereby optimizing the power generation curve to match the electricity price period and improving the revenue; the adjusted inverter operating parameters directly control the power output, achieving fast response and accurate control, enhancing the adaptability and efficiency of the system, and ensuring optimal performance in a changing environment.

[0014] Further provided is that the group string connection state of the east-west side photovoltaic array is automatically adjusted according to real-time irradiance changes and shadow blocking through a dynamic group string switching method, which includes the following steps: predict the future power generation capacity of the east-west side photovoltaic array based on a short-term irradiance prediction algorithm; wherein the short-term irradiance prediction algorithm is based on historical irradiance data, real-time weather data and a machine learning model to predict the change in irradiance intensity within a preset time in the future; the historical irradiance data comes from a local weather station or a satellite irradiance database, and the real-time weather data includes cloud cover, temperature and humidity; real-time monitor the irradiance intensity and group string power generation of the east-west side photovoltaic array, and identify irradiance significantly decreased or shadow blocking events based on the irradiance intensity and group string power generation; wherein the irradiance significantly decreased or shadow blocking events are determined by comparing the real-time irradiance intensity with a preset irradiance threshold or by analyzing the sudden change of the group string power generation; the preset irradiance threshold is dynamically adjusted based on the typical irradiance level and seasonal characteristics of the east-west side photovoltaic array; When a significant irradiance drop or shadow obstruction event is identified, the affected string is automatically disconnected; wherein the affected string is a string in the east-west PV array with a significant drop in power generation, and the disconnection is achieved by controlling the switching devices at the string level; the switching devices at the string level include relays or solid-state switches for isolating the affected string to reduce system losses; When the light conditions recover, the previously disconnected string is automatically reconnected; wherein the recovery of light conditions is determined by monitoring the irradiance in real time and comparing the real-time irradiance with a preset recovery threshold, and the reconnection is achieved by controlling the switching devices at the string level; the preset recovery threshold is set based on the output of the short-term irradiance prediction algorithm and the historical performance data of the east-west PV array; Based on the dynamic string switching module, the overall power generation efficiency of the east-west PV array is optimized; wherein the dynamic string switching module adjusts the string connection state to maintain high efficiency power generation under irradiance changes and shadow obstruction, and cooperates with the power generation-price coupling model to ensure that the power generation curve matches the peak-valley price period.

[0015] By adopting the above technical solutions, the future power generation capacity is predicted based on the short-term irradiance prediction algorithm, the irradiance intensity change is predicted using historical irradiance data, real-time weather data and machine learning models, the predictability of the system for future conditions is improved, the string state is adjusted in advance, and the power generation efficiency is optimized; the irradiance intensity and the power generation of the string are monitored in real time, the irradiance significantly decreases or the shadow obstruction event is identified, the real-time irradiance is compared with the preset threshold or the power mutation is analyzed to ensure the timeliness and accuracy of event detection, and the risk of misjudgment and omission is reduced; when the event is identified, the affected string is automatically disconnected, the isolation is achieved by the switching devices at the string level such as relays or solid-state switches, the system losses are reduced, the local problem does not affect the overall efficiency, and the reliability and power generation of the system are improved; when the light conditions recover, the string is automatically reconnected, the comparison between the real-time irradiance and the preset recovery threshold ensures that the system recovers the maximum power generation capacity in time, reduces the need for manual intervention, and improves the automation level; the dynamic string switching module optimizes the overall power generation efficiency, maintains high efficiency power generation under irradiance changes and shadow obstruction, and cooperates with the power generation-price coupling model to ensure that the power generation curve matches the peak-valley price period, thereby improving the income and reliability, and enhancing the adaptability of the system to complex environments.

[0016] Further, the power generation-price coupling model is updated periodically to reflect changes in electricity price policies and PV array performance degradation, and a sensitivity analysis is performed to evaluate the impact of the narrowing of peak-valley price difference on power generation income, which includes the following steps: periodically triggering a model updating process; wherein the periodic triggering is based on a preset updating period or an external event, the preset updating period is monthly, quarterly or annually, and the external event includes a power price policy release event or a photovoltaic array performance detection report generation event; obtaining updated power price policy information and photovoltaic array performance attenuation data; wherein the updated power price policy information is obtained from a local power grid operator or a power market platform; and the photovoltaic array performance attenuation data is obtained based on analysis of power generation power data and photovoltaic component performance parameters in the monitoring database, including component efficiency attenuation rate and temperature coefficient change; based on the updated power price policy information and photovoltaic array performance attenuation data, performing a basic update on the power generation-power price coupling model to generate a benchmark power distribution strategy and a benchmark inverter operating parameter; wherein the basic update includes updating the power price curve and photovoltaic component performance parameters in the power generation-power price coupling model.

[0017] By adopting the above technical solution, the model is updated periodically based on a preset updating period or an external event, such as monthly, quarterly or annually or a power price policy release event, ensuring that the model adapts to changes in time, maintains relevance, and avoids control bias due to outdated data; obtaining updated power price policy information and photovoltaic array performance attenuation data, obtaining power price information from authoritative sources, and based on monitoring data performance attenuation, ensuring the accuracy and reality of the data, providing reliable input for model updating; performing a basic update on the model, updating the power price curve and photovoltaic component performance parameters, generating a benchmark power distribution strategy and a benchmark inverter operating parameter, so that the control strategy is always based on the latest information, optimizing long-term performance, avoiding loss of revenue due to outdated data, and improving the adaptability and sustainability of the system.

[0018] Further, the power generation-power price coupling model is updated periodically to reflect changes in power price policy and photovoltaic array performance attenuation, and a sensitivity analysis is performed to evaluate the impact of peak-valley price difference reduction on power generation revenue. Specifically, the following steps are included: performing a sensitivity analysis to evaluate the impact of peak-valley price difference reduction on power generation revenue; wherein the sensitivity analysis simulates changes in power generation revenue under different peak-valley price difference scenarios, calculates a revenue sensitivity index, and the revenue sensitivity index is an elasticity coefficient of power generation revenue with respect to peak-valley price difference; Based on the income sensitivity index obtained by the sensitivity analysis, the benchmark power distribution strategy and the benchmark inverter operation parameter are defensively optimized to generate the final applied power distribution strategy and inverter operation parameter; wherein the defensive optimization includes increasing the power output target value of the benchmark power distribution strategy in the peak period, or adjusting the inverter output power limit value in the benchmark inverter operation parameter, so as to alleviate the negative impact of the narrowing of the peak-valley price difference on the power generation income.

[0019] By adopting the above technical solution, the change of power generation income under different peak-valley price difference scenarios is simulated, the income sensitivity index such as the elasticity coefficient is calculated, the quantitative evaluation of risk factors is provided, the risk management capability of the system is enhanced, and the countermeasures can be prepared in advance; based on the sensitivity analysis result, the benchmark power distribution strategy and the benchmark inverter operation parameter are defensively optimized to generate the final applied strategy and parameter, such as increasing the power output target value in the peak period or adjusting the inverter output power limit value, so as to alleviate the negative impact of the narrowing of the peak-valley price difference, ensure that the system can still maintain high income under adverse conditions, improve the robustness and adaptability of the system, and prolong the economic service life of the equipment.

[0020] Further setting is that the east-west asymmetric layout characteristics of the photovoltaic array specifically include: The east and west photovoltaic arrays are composed of power generation components, and the power generation components are double-sided power generation components or single-sided single-crystal silicon components.

[0021] By adopting the above technical solution, the double-sided power generation component can use the morning low-angle sunlight and scattered light to improve the overall power generation efficiency, fully utilize the morning irradiation condition, increase the power generation capacity, and especially in the morning period that may appear in the peak period of electricity price, improve the income potential; the single-sided single-crystal silicon component can resist the influence of the high-temperature environment in the afternoon, reduce the negative influence of temperature on the power generation efficiency, improve the power generation stability in the afternoon period, and ensure that a high output can be maintained under high-temperature conditions.

[0022] Further setting is that the east-west asymmetric layout characteristics of the photovoltaic array specifically further include: The capacity ratio range of the east photovoltaic array is 1.3-1.5, and the capacity ratio range of the west photovoltaic array is 1.1-1.3.

[0023] By adopting the above technical solution, the differentiated capacity ratio design considers the differences in the east and west irradiation conditions and component characteristics, the higher capacity ratio of the east side utilizes more morning irradiation to maximize the morning power generation capacity, and the lower capacity ratio of the west side adapts to the high temperature and irradiation change in the afternoon.

[0024] 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

[0025] Figure 1 This is a schematic diagram of the main process of an embodiment; Figure 2 In 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. 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. 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. 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

[0026] The present invention will be further described in detail below with reference to the accompanying drawings.

[0027] As attached Figures 1-5 As shown; This embodiment discloses a photovoltaic power output optimization control method that integrates peak-valley electricity price time characteristics, including the following steps: 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.

[0028] The implementation process is as follows: first, obtain the time-of-use electricity price policy file from the local grid operator or the public interface or data file of the electricity market platform, which is usually stored in a structured format such as XML, JSON or CSV, containing the start and end timestamps of the peak period, flat period and valley period and the corresponding electricity value. Extract these key parameters through the parsing module, and generate a continuous electricity price curve based on the time series, which reflects the electricity price fluctuations in different periods, providing basic input for subsequent power generation revenue optimization. At the same time, the system combines the east-west asymmetric layout characteristics of the photovoltaic array, the east photovoltaic array uses double-sided generating components to utilize the morning low-angle sunlight and scattered light to improve overall power generation efficiency, and the west photovoltaic array uses monocrystalline silicon components to resist the influence of high-temperature environment in the afternoon. 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 irradiation characteristics and temperature tolerance requirements of different orientations.

[0029] Further, the east photovoltaic array tilt angle is set to 10°-15°, and the west photovoltaic array tilt angle is set to 15°-20°; the specific values of the east-west photovoltaic array tilt angle are determined by simulating and comparing multiple scenarios through photovoltaic simulation software, taking into account the local latitude, typical meteorological year data and roof structure constraints, and optimizing to maximize power generation revenue; the tilt angle design takes into account the structural safety requirements including wind load and snow load, and the support system meets the additional load requirement of not less than 25 kg / m².

[0030] In terms of power generation simulation, a power generation prediction model is constructed based on historical irradiation data and photovoltaic component performance parameters. Historical irradiation data comes from local weather stations or satellite irradiation databases, including multi-year hourly global horizontal irradiance and diffuse irradiance records, and combined with the installation tilt angle, azimuth angle and geographic coordinates of the photovoltaic array for coordinate conversion and inclined plane correction to accurately reflect the actual light receiving conditions of the east and west photovoltaic arrays. Photovoltaic component performance parameters include component efficiency, temperature coefficient and attenuation rate, which are obtained through technical specification tables provided by component manufacturers or field measurement data, and used to calculate the actual output efficiency of photovoltaic components under different environmental temperatures. Through simulation, the hourly power generation curves of the east and west photovoltaic arrays in different seasons (such as spring, summer, autumn and winter) and weather types (such as sunny, cloudy and rainy) are simulated, taking into account factors such as component temperature change, shadow shading loss and inverter conversion efficiency, to generate high-precision power generation time series data.

[0031] Then, the power generation curves of the east and west photovoltaic arrays are time-series superimposed with the electricity price curve to maximize the net income or minimize the cost per kilowatt-hour in the whole life cycle of the project, and the initial power distribution strategy and inverter operating parameters of the east and west photovoltaic arrays are generated through an optimization algorithm. The determination of the optimization objective function is crucial: if the maximum net income in the whole life cycle of the project is taken as the objective, the objective function is based on the maximization of the net value after the total power generation income is subtracted from the investment cost and operation and maintenance cost, wherein the total power generation income is obtained by integrating the product of power generation and corresponding electricity price, and the investment cost includes the initial investment of photovoltaic arrays and inverters, and the operation and maintenance cost includes cleaning, maintenance and monitoring fees; if the minimum cost per kilowatt-hour is taken as the objective, the objective function is based on the minimization of the value after the total power generation is divided by the total cost, and the total cost also covers the investment and operation and maintenance parts. The set of decision variables includes the east inverter operating parameters and the west inverter operating parameters, wherein the east inverter operating parameters include the east inverter maximum power point tracking voltage range and the east inverter output power limit, and the west inverter operating parameters include the west inverter maximum power point tracking voltage range and the west inverter output power limit. The setting of the constraint conditions ensures the feasibility of the system: the inverter maximum power point tracking voltage range constraint is based on the inverter technical specifications to prevent the voltage from exceeding the safe operating interval; the inverter output power limit constraint is based on the 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 irradiation conditions, avoiding overload or efficiency loss. The optimization algorithm adopts genetic algorithm, particle swarm optimization or gradient descent algorithm, for example, the genetic algorithm iteratively optimizes the decision variables through selection, crossover and mutation operations, the particle swarm optimization searches for the optimal solution based on swarm intelligence, and the gradient descent algorithm uses the gradient of the objective function for local optimization. The algorithm iteratively optimizes the set of decision variables under the premise of meeting the constraint conditions to obtain the optimal solution, which corresponds to a set of power distribution strategies and inverter operating parameters of the east and west photovoltaic arrays; the power distribution strategy is determined by the optimized inverter operating parameters, including the power output distribution scheme of the east and west photovoltaic arrays in the peak period, the flat period and the valley period, for example, the east array output is preferentially improved in the peak period to utilize the high morning electricity price, the east and west outputs are balanced in the flat period, and the output is reduced in the valley period to save inverter loss; the inverter operating parameters include the maximum power point tracking voltage range and the output power limit of the inverter, which are dynamically adjusted through the optimization algorithm to ensure the optimal matching of the power generation curve and the electricity price curve, maximizing the power generation income or minimizing the cost per kilowatt-hour.

[0032] S2, real-time monitoring of the irradiance and power generation of the photovoltaic array; wherein the photovoltaic array is composed of a plurality of groups of strings, the irradiance is obtained by a irradiance sensor, and the power generation is obtained by a group string level current-voltage sensor.

[0033] The specific implementation process is as follows: the basic irradiance data is collected in real time by deploying irradiance sensors at key monitoring points of the east-west photovoltaic array, the irradiance sensors adopt at least one high-precision photovoltaic radiometer conforming to the IEC 61724 standard, the spectral response range of which covers 300-1100nm, and the installation position is strictly perpendicular to the surface of the photovoltaic module to accurately measure the total irradiance incident on the module plane. In order to avoid sensor installation deviation and environmental interference, the system introduces an irradiance calibration coefficient to correct the original measurement value: first, the theoretical irradiance receiving efficiency is calculated based on the actual installation inclination of the photovoltaic array (the inclination of the east photovoltaic array is set to 10°-15°, and the inclination of the west photovoltaic array is set to 15°-20°) and the azimuth (the east photovoltaic array corresponds to 90° / the west photovoltaic array corresponds to 270°), and then a dynamically updated irradiance calibration coefficient matrix is generated through regression analysis of the readouts of the standard radiometer and the actual power generation data on site (the irradiance calibration coefficient of the east photovoltaic array ranges from 0.98 to 1.02, and the irradiance calibration coefficient of the west photovoltaic array ranges from 0.97 to 1.03). The corrected irradiance intensity calculation formula is: ; wherein, represents the corrected irradiance intensity; represents the corresponding basic irradiance intensity; represents the corresponding irradiance calibration coefficient.

[0034] In terms of power generation, the system collects the output characteristics of each string in real time through high-precision current and voltage sensors (accuracy level 0.5 level) at the string level, the current sensor measures 0-15A range direct current by using Hall effect principle, and the voltage sensor measures 0-1000V direct voltage by using resistance voltage division method. The string power generation is calculated by multiplying the real-time current and voltage, and is subjected to temperature compensation processing (based on PT1000 temperature sensor data to correct the influence of module temperature). All monitoring data (including corrected irradiance intensity, string power generation, and module backboard temperature) are associated with high-precision time stamps (error ±1ms) generated by Beidou / GPS clock, transmitted to edge computing gateway through Modbus-RTU protocol, stored in time series database InfluxDB after data validity verification (eliminate abnormal values and jump points), the storage period is 1 minute per piece, and the retention period is not less than 3 years. The database is deployed in a distributed architecture, the master node is responsible for real-time data writing, and the slave node supports historical data query and analysis interface calling, ensuring the integrity and traceability of the monitoring data.

[0035] S3, based on the generated power distribution strategy and inverter operation parameters, and combined with the real-time monitored irradiance and power generation, dynamically adjusts the power output of the east-west photovoltaic array to make the power generation curve match the peak-valley electricity price period, so as to maximize the power generation benefit.

[0036] The implementation process is as follows: first, based on the power allocation strategy, the power output target value of the east-west photovoltaic array corresponding to the current peak-valley electricity price period is obtained. The power allocation strategy includes the power output allocation scheme of the east-west photovoltaic array in the peak period, the flat period and the valley period. For example, in the peak period (such as the high electricity price period defined by the local power grid, usually 8:00-12:00 in the morning and 18:00-22:00 in the evening), the power output target value may be set to more than 90% of the east photovoltaic array output power limit to take advantage of the morning high electricity price; in the flat period (such as 12:00-18:00), the power output target value is adjusted to the east-west balanced output; in the valley period (such as 22:00-8:00 the next day), the power output target value is reduced to the minimum running power to save inverter loss. The system automatically identifies the current belonging to the peak-valley electricity price period through the real-time clock module and the electricity price strategy database, and queries the corresponding power output target value. These target values are stored in the form of preset power percentage or absolute power value, and are dynamically calibrated according to the season and weather type. Next, the system obtains the predicted power generation of the east-west photovoltaic array based on the real-time monitored irradiance through the photovoltaic power generation power calculation model. The photovoltaic power generation power calculation model is based on the conversion efficiency, temperature coefficient and real-time irradiance of the photovoltaic module. Specifically, the calculation formula of the predicted power generation is: ; wherein, represents the predicted power generation (unit: kilowatt), represents the corrected irradiance (unit: W / m²), which is obtained by the irradiation sensor and corrected by the calibration coefficient; represents the effective light receiving area of the photovoltaic array (unit: m²), which is determined according to the installation layout and component quantity of the east-west photovoltaic array; represents the nominal conversion efficiency of the photovoltaic module, which is based on the component technical parameters (such as the east double-sided power generation component efficiency is about 21%, and the west single-crystal silicon component efficiency is about 19.5%); represents the temperature coefficient (unit: % / °C), the typical value is -0.35% / °C; represents the real-time temperature of the photovoltaic module (unit: °C), which is monitored by the backboard temperature sensor; represents the reference temperature (usually 25°C). The model is run by the edge computing gateway in real time, and the predicted value is updated every 5 minutes, and combined with historical irradiation data and weather forecast for rolling optimization to improve the prediction accuracy.

[0037] Then, the system takes the power output target value as the final control target, and combines the current power generation capacity represented by the predicted power generation, based on the real-time monitored power generation and the power output target value, the power adjustment algorithm dynamically adjusts the inverter operating parameters of the east-west photovoltaic array. The power adjustment algorithm adopts proportional-integral-derivative control algorithm or model predictive control algorithm, which is used to minimize the power generation deviation and make the power output of the east-west photovoltaic array close to the power output target value within the physically feasible range. In the embodiment, the proportional-integral-derivative control algorithm is preferably used; for example, when the proportional-integral-derivative control algorithm is used, the control error defined as the difference between the real-time monitored power generation and the power output target value : ; the output of the proportional-integral-derivative control algorithm is used to adjust the maximum power point tracking voltage range or output power limit value of the inverter, and its expression is: ; wherein , and are proportional, integral and differential gain coefficients, which are determined by field debugging and simulation optimization (for example, the of the east photovoltaic array is set to 0.8, is set to 0.1, is set to 0.05; the of the west photovoltaic array is set to 0.7, is set to 0.15, is set to 0.04 to ensure fast and stable system response. For example, when using a model predictive control algorithm, the model predictive control algorithm employs a rolling optimization method based on a linear state-space model and a quadratic program. Specifically, the model predictive control algorithm takes the power output of the east and west photovoltaic arrays as the system state, and the inverter operating parameters (such as maximum power point tracking reference values) as control inputs, and constructs a quadratic performance index with the goal of maximizing power generation revenue and minimizing power fluctuations over a future number of sampling periods (such as a prediction horizon of 10 periods). At each control period, using the real-time updated prediction sequence of irradiance and temperature, a constrained quadratic programming problem is solved online to obtain the optimal inverter control sequence, and only the first step control instruction in the sequence is sent to the inverter for execution, and the optimization process is repeated in the next period to achieve forward-looking dynamic power adjustment. The system is based on short-term prediction data such as irradiance, temperature, and peak-valley electricity price information for a future period (such as 15-30 minutes), and solves a rolling optimization problem with the goal of maximizing power generation revenue at multiple future times. The system performs a control cycle every 30 seconds, calculates the control amount in real time and sends it to the inverter controller, and dynamically adjusts the inverter operating parameters, such as adjusting the east inverter maximum power point tracking voltage range to 450-800V and the west inverter output power limit to 85% of the rated power, to smooth the power fluctuations and reduce the deviation.

[0038] During dynamic adjustment, the system also considers physical constraints such as inverter maximum power point tracking voltage range constraints (east inverter voltage range limited to 400-820V and west inverter voltage range limited to 420-800V) and inverter output power limit constraints (based on the simulated power generation power curve of the east and west photovoltaic arrays, to ensure that the output power does not exceed the maximum possible power generation power under given irradiance conditions). For example, when the real-time irradiance intensity drops suddenly, the predicted power generation power may be lower than the power output target value, and the system reduces the inverter output power limit through a proportional-integral-derivative control algorithm to avoid overload; conversely, when the irradiance is sufficient, the output is increased to approach the target value.

[0039] 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 the peak-valley electricity price period. For example, during the peak period, the system preferentially increases the output power of the east photovoltaic array to take advantage of the high morning irradiance and high electricity price; during the flat period, the east and west outputs are balanced to maintain network stability; during the valley period, the output is reduced to reduce inverter loss and grid impact. The entire process is closed-loop feedback through real-time data to maximize power generation revenue, while the system records an adjustment log every 15 minutes, including power deviation, control action, and revenue indicators, for subsequent performance analysis and model optimization.

[0040] S4, by a dynamic string switching method, automatically adjusting the string connection state of the east and west photovoltaic arrays according to real-time irradiance changes and shadow blocking; wherein the dynamic string switching is based on a short-term irradiance prediction algorithm to predict future power generation capacity, and when irradiance significantly decreases or shadow blocking occurs, the affected string is automatically removed and reconnected when the light conditions recover.

[0041] The implementation process is as follows: in the implementation of the dynamic group string switching method, the system first predicts the future power generation capacity of the east-west photovoltaic array based on a short-term irradiance prediction algorithm. The short-term irradiance prediction algorithm integrates historical irradiance data, real-time weather data and machine learning models, where the historical irradiance data comes from local weather stations or satellite irradiance databases, including multi-year hourly global horizontal irradiance and scattered irradiance records, real-time weather data is obtained through weather monitoring stations deployed on site, including cloud cover, temperature, humidity, wind speed and atmospheric pressure and other multi-dimensional parameters. The machine learning model uses a long short-term memory network architecture, which includes time series irradiance data, weather variables and seasonal characteristics in the input layer, 128 neurons in the hidden layer, and an output layer to predict the irradiance intensity change curve within 30 minutes to 2 hours in the future; during model training, the historical one-year data is used as the training set, the root mean square error is used as the loss function, and the Adam optimizer is used for iterative optimization until the prediction accuracy reaches more than 90%. The prediction result is output in the form of minute-by-minute irradiance value and stored in the cache of the edge computing gateway for subsequent power generation capacity evaluation and decision support. Real-time monitoring involves continuously collecting data from irradiance sensors and string-level current and voltage sensors installed at key locations on the east-west photovoltaic array. The irradiance sensor uses a high-precision photovoltaic radiometer, which is installed vertically to the surface of the photovoltaic module, with a measurement range of 0-1500 W / m² and an accuracy of ±2%; the string power is obtained in real time by a Hall effect current sensor and a resistance voltage divider voltage sensor, with a sampling frequency of 1 Hz, and the data is processed by temperature compensation and noise filtering. The comparison between real-time irradiance intensity and the preset irradiance threshold is used to identify significant irradiance decline events, and the preset irradiance threshold is dynamically adjusted according to the typical irradiance level and seasonal characteristics of the east-west photovoltaic array, for example, in summer, the irradiance threshold of the east photovoltaic array is set to 600 W / m², and the west photovoltaic array is set to 550 W / m², and when the real-time irradiance intensity decreases by more than 30% in 5 minutes, it is determined to be a significant irradiance decline event. Shadow shading events are identified by analyzing the sudden change in string power: the system calculates the power difference between each string and the power of adjacent strings, and if the difference exceeds 15% and is accompanied by a simultaneous decrease in irradiance, the string is marked as an affected string; at the same time, a sliding window mechanism is introduced to analyze the trend of the power data to distinguish between transient fluctuations and persistent shading.

[0042] When a significant irradiance drop or shadowing event is identified, an automatic string tripping operation is triggered. The determination of affected strings is based on real-time monitoring data of string power generation, combined with the output of short-term irradiance prediction algorithm, to ensure that only the truly affected strings are isolated. The tripping operation is achieved by controlling the switching devices at string level, which include high-reliability relays or solid-state switches with a rated current of 15 A, a rated voltage of 1000 V DC, and a switching time of less than 100 milliseconds. The system sends tripping instructions to the switch controller through Modbus-RTU or CAN bus protocol, which contains the string identifier and action code; during the tripping process, the system records the string state change log, including timestamp, string number, tripping reason and operation result, and updates the topology connection diagram of the photovoltaic array in real time. After tripping, the affected strings are isolated from the main circuit to avoid lowering the overall array voltage and increasing system loss, while the system adjusts the maximum power point tracking parameters through the inverter communication interface to adapt to the new array configuration.

[0043] When the light conditions recover, the system automatically reconnects the previously tripped strings. The determination of light condition recovery is based on the comparison of real-time monitored irradiance and the preset recovery threshold, which is set by the output of the short-term irradiance prediction algorithm and the historical performance data of the east and west photovoltaic arrays, for example, when the real-time irradiance is stable above the threshold for 10 minutes (the east photovoltaic array recovery threshold is set to 500 W / m², and the west photovoltaic array recovery threshold is set to 480 W / m²) and there is no significant downward trend in the next 30 minutes, the reconnection operation is triggered. The reconnection process is performed through the same string-level switching devices, the system first performs a pre-check on the strings to be reconnected, including checking whether their open-circuit voltage and short-circuit current are within the normal range, to avoid the risk of reverse current or shock; after the pre-check is passed, the controller sends a reconnection instruction, and after the switch is closed, the string is reconnected to the circuit, and the system updates the routing state and restores the power collection of the string. The reconnection operation adopts a soft start strategy, which gradually increases the inverter's absorbed power to avoid sudden current surge, ensuring smooth transition of the system.

[0044] The dynamic group string switching module is used to optimize the overall power generation efficiency of the east-west photovoltaic array. The dynamic group string switching module adjusts the connection state of the group string in real time to maintain high-efficiency power generation of the photovoltaic array under varying irradiance and shadow shielding conditions. The core logic is to maximize the output of available group strings and minimize mismatch loss. The switching decision is coupled with the power generation-price coupling model to ensure that the power generation curve matches the peak and valley price period: for example, during the peak period, the system preferentially maintains the full connection of the group strings of the east photovoltaic array to take advantage of the high price, and does not remove the group strings even if they are slightly shaded; during the flat or valley period, the switching is strictly based on the efficiency principle. The system performs global optimization calculation every 5 minutes, re-evaluates the group string switching scheme based on short-term irradiance prediction, real-time load and price strategy, and issues decision instructions to the field equipment through an anti-interference communication protocol. The whole process realizes the intelligent self-healing and efficiency improvement of the photovoltaic array, and generates performance reports at the edge gateway, including switching times, power generation gain and income impact indicators, which are used for subsequent model calibration and operation and maintenance analysis.

[0045] S5, periodically updating the power generation-price coupling model to reflect changes in electricity price policy and photovoltaic array performance degradation, and performing sensitivity analysis to evaluate the impact of narrowing the peak-valley price difference on power generation income.

[0046] The specific implementation process is as follows: the model updating process is triggered by a preset update period or an external event driving mechanism, where the preset update period can be configured to perform a comprehensive update once a month, once a quarter or once a year, and the external events include the event of the local power grid issuing a new time-of-use electricity price policy document or the event of the operation and maintenance platform generating a photovoltaic array performance detection report. When the trigger condition is 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, which is transmitted in a structured format (such as JSON or XML) and contains the start and end times of the peak period, the flat period and the valley period and the corresponding electricity value, and the key parameters are extracted by the data parsing module to regenerate the electricity price curve. At the same time, the system analyzes the photovoltaic array performance degradation data based on the historical power generation data (such as time series data collected by group string level current voltage sensors) stored in the monitoring database and the photovoltaic module field detection report, including the annual degradation rate of the component efficiency (usually ), temperature coefficient drift value and backboard light transmittance change amount, which are calculated by linear regression model and initial performance parameters.

[0047] After acquiring the updated tariff policy information and the photovoltaic array performance degradation data, the system performs a basic update on the power generation-tariff coupling model: first, replace the tariff curve in the model with the latest version, and correct the performance parameters of the photovoltaic components based on the degradation data, for example, adjust the efficiency of the east-side double-sided power generation component from the initial 21% to 20.6%, and adjust the efficiency of the west-side monocrystalline silicon component from 19.5% to 19.1%; then re-run the power generation power simulation module to generate the updated hourly power generation power curve of the east-west photovoltaic array in combination with the historical irradiance data. The model uses the life cycle cost-benefit algorithm to re-calculate the optimal solution, generating the baseline power distribution strategy and the baseline inverter operating parameters, wherein the baseline power distribution strategy includes adjusting the output proportion of the east-west photovoltaic array in the peak period to 85% for the east-side photovoltaic array and 75% for the west-side photovoltaic array, adjusting the output proportion of the east-west photovoltaic array in the flat period to 70% for the east-side photovoltaic array and 65% for the west-side photovoltaic array, and adjusting the output proportion of the east-west photovoltaic array in the valley period to 30% for the east-side photovoltaic array and 25% for the west-side photovoltaic array; the baseline inverter operating parameters include updating the maximum power point tracking voltage range of the east-side inverter to 440-810V, setting the output power limit to 88% of the rated capacity, adjusting the voltage range of the west-side inverter to 430-790V, and setting the output power limit to 82% of the rated capacity.

[0048] Then the system performs a sensitivity analysis to evaluate the impact of the narrowing of the peak-valley price difference on the power generation income: by simulating different price difference narrowing scenarios (such as a 10%-30% drop in peak electricity price and a 5%-15% increase in valley electricity price), the system calculates the change in power generation income in each scenario, and uses the elasticity coefficient algorithm to calculate the income sensitivity index, which is defined as the ratio of the change in power generation income to the change in the peak-valley price difference. In the specific calculation, the system constructs a multi-dimensional sensitivity analysis matrix, the matrix input includes the price difference narrowing interval, the irradiance condition type and the load characteristics, and the output is the income sensitivity curve and the critical price difference threshold (such as when the price difference is narrowed to 0.35 yuan / kWh, the income decreases significantly). Based on the results of the sensitivity analysis, the system implements defensive optimization on the baseline power distribution strategy and the baseline inverter operating parameters: for the price difference narrowing scenario, increase the power output target value in the peak period (such as increasing the output of the east-side photovoltaic array to 90% and the output of the west-side photovoltaic array to 80%), and adjust the output power limit in the inverter operating parameters (increase the limit of the east-side inverter to 90% and the limit of the west-side inverter to 85%) to maximize the peak period income; at the same time, optimize the power distribution proportion in the flat period and increase the output weight of the east-side array to take advantage of its morning power generation advantage. The final power distribution strategy and inverter operating parameters are deployed to the on-site controller through the strategy deployment module, and the optimization log is recorded for subsequent iterative optimization.

[0049] The embodiments are only used to explain the present application, and are not used to limit the present application, and any modification without creative contribution made by the person skilled in the art according to the embodiments after reading the specification is protected by the patent law as long as it is within the scope of the claims of the present application.

Claims

1. A method for optimizing control of photovoltaic power output with fusion of peak and off-peak tariff period characteristics, characterized in that, The method comprises the following steps: Based on the local power grid peak valley electricity price period division and electricity price information, combined with the east-west asymmetric layout characteristics of the photovoltaic array, a power generation-price coupling model is established and run to generate the power distribution strategy and inverter operation parameters of the east-west photovoltaic array; wherein the peak valley electricity price period includes peak period, flat period and valley period; Real-time monitoring of the irradiance and power generation of the photovoltaic array; wherein the photovoltaic array is composed of multiple groups of strings, the irradiance is obtained by a radiation sensor, and the power generation is obtained by a group string level current-voltage sensor; Based on the generated power distribution strategy and inverter operation parameters, and combined with the real-time monitored irradiance and power generation, the power output of the east-west photovoltaic array is dynamically adjusted to make the power generation curve match the peak valley electricity price period, so as to maximize the power generation benefit; Through a dynamic string switching method, the connection state of the east-west photovoltaic array is automatically adjusted according to the real-time irradiance change and shadow shielding condition; wherein the dynamic string switching is based on a short-term irradiance prediction algorithm to predict the future power generation capacity, and when the irradiance significantly decreases or shadow shielding occurs, the affected string is automatically cut off, and when the light condition recovers, the string is automatically reconnected. The power generation-price coupling model is updated regularly to reflect changes in electricity price policy and photovoltaic array performance degradation, and sensitivity analysis is performed to evaluate the impact of the narrowing of the peak valley price difference on power generation benefit. 2.The method of claim 1, wherein, Based on the local power grid peak valley electricity price period division and electricity price information, combined with the east-west asymmetric layout characteristics of the photovoltaic array, a power generation-price coupling model is established and run to generate the power distribution strategy and inverter operation parameters, which specifically comprises the following steps: Obtain the time-of-use electricity price policy file from the local power grid operator or electricity market platform, parse and extract the start and end times of the peak period, flat period and valley period, and the corresponding electricity value, and generate an electricity price curve; Based on historical irradiance data and photovoltaic module performance parameters, simulate the hourly power generation curve of the east-west photovoltaic array under different seasons and weather conditions; wherein the historical irradiance data comes from local weather stations or satellite irradiance databases, and the photovoltaic module performance parameters include module efficiency, temperature coefficient and attenuation rate; Superimpose the simulated power generation curve of the east-west photovoltaic array on the electricity price curve in time sequence, and generate the initial power distribution strategy and inverter operation parameters of the east-west photovoltaic array by optimization algorithm, with the goal of maximizing net benefit or minimizing cost per kilowatt-hour in the whole life cycle of the project; wherein the power distribution strategy includes the power output distribution scheme of the east-west photovoltaic array in the peak period, flat period and valley period, and the inverter operation parameters include the maximum power point tracking voltage range and output power limit of the inverter; wherein the optimization algorithm includes genetic algorithm, particle swarm optimization algorithm or gradient descent algorithm. 3.The method of claim 2, wherein, Generate the initial power distribution strategy and inverter operation parameters of the east-west photovoltaic array by optimization algorithm with the goal of maximizing net benefit or minimizing cost per kilowatt-hour in the whole life cycle of the project, which specifically comprises the following steps: determining an optimization objective function; wherein the optimization objective function comprises a maximum net benefit objective function or a minimum cost per kilowatt-hour objective function in a project life cycle, the maximum net benefit objective function in the project life cycle is to maximize the net value based on the total power generation benefit minus the investment cost and operation and maintenance cost, and the minimum cost per kilowatt-hour objective function is to minimize the value based on the total power generation divided by the total cost; determining a decision variable set; wherein the decision variable set comprises east side inverter operating parameters and west side inverter operating parameters; the east side inverter operating parameters comprise east side inverter maximum power point tracking voltage range and east side inverter output power limit, and the west side inverter operating parameters comprise west side inverter maximum power point tracking voltage range and west side inverter output power limit; setting a constraint condition; wherein the constraint condition comprises inverter maximum power point tracking voltage range constraint and inverter output power limit constraint; the inverter output power limit constraint is set based on the simulated power generation power curve of the east and west side photovoltaic arrays to ensure that the output power limit does not exceed the maximum possible power generation power under given irradiation conditions; solving the optimization objective function by using an optimization algorithm; wherein the optimization algorithm is used to iteratively optimize the decision variable set under the premise of meeting the constraint condition to obtain an optimal solution; the optimal solution corresponds to a set of power distribution strategies and inverter operating parameters of the east and west side photovoltaic arrays; the power distribution strategy is determined by the optimized inverter operating parameters.

4. The method of claim 1, wherein the method further comprises: The real-time monitoring of the irradiation intensity and the power generation of the photovoltaic array specifically comprises the following steps: acquiring the basic irradiation intensity corresponding to the east and west side photovoltaic arrays based on arranged irradiation sensors, wherein the irradiation sensors comprise at least one photovoltaic radiometer for measuring the total irradiance perpendicular to the surface of the photovoltaic component; multiplying the basic irradiation intensity corresponding to the east and west side photovoltaic arrays by a preset irradiation calibration coefficient to obtain a corrected irradiation intensity; wherein the irradiation calibration coefficient is determined based on the installation inclination and azimuth angle of the photovoltaic array, and is used to eliminate the measurement deviation caused by the installation position and environment of the sensor; the power generation is calculated based on the string group level current-voltage sensor acquired string group power generation; storing the corrected irradiation intensity and the power generation in association with the time stamp to a monitoring database.

5. The method of claim 1, wherein the method further comprises: Based on the generated power distribution strategy and inverter operating parameters, and in combination with the real-time monitored irradiation intensity and power generation, the power output of the east and west side photovoltaic arrays is dynamically adjusted to match the power generation curve with the peak-valley electricity price period to maximize the power generation benefit, which specifically comprises the following steps: based on the power distribution strategy, acquiring the power output target value of the east and west side photovoltaic arrays corresponding to the current belonging peak-valley electricity price period; based on the real-time monitored irradiation intensity, obtaining the predicted power generation of the east and west side photovoltaic arrays through a photovoltaic power generation power calculation model; wherein the photovoltaic power generation power calculation model calculates the predicted power generation based on the conversion efficiency, temperature coefficient and real-time irradiation intensity of the photovoltaic component; The power output target value is taken as the final control target, and the current power generation capacity represented by the predicted power generation is combined. Based on the real-time monitored power generation and the power output target value, the inverter operating parameters of the east-west photovoltaic array are dynamically adjusted through a power adjustment algorithm; wherein the power adjustment algorithm is a proportional-integral-derivative control algorithm or a model predictive control algorithm, which is used to minimize the power generation deviation and make the power output of the east-west photovoltaic array close to the power output target value within the physically feasible range; Based on the adjusted inverter operating parameters, the power output of the east-west photovoltaic array is controlled to make the power generation curve match the peak-valley electricity price period, so as to maximize the power generation income.

6. The method of claim 1, wherein the method further comprises: Through the dynamic string switching method, the connection state of the east-west photovoltaic array is automatically adjusted according to the real-time irradiance change and shadow shielding condition, which includes the following steps: Based on the short-term irradiance prediction algorithm, the future power generation capacity of the east-west photovoltaic array is predicted; wherein the short-term irradiance prediction algorithm is based on historical irradiance data, real-time weather data and machine learning model to predict the change of irradiance intensity in the future preset time; the historical irradiance data comes from local weather station or satellite irradiance database, and the real-time weather data includes cloud cover, temperature and humidity; The irradiance intensity and string power generation of the east-west photovoltaic array are monitored in real time, and the irradiance significant drop or shadow shielding event is identified based on the irradiance intensity and string power generation; wherein the irradiance significant drop or shadow shielding event is determined by comparing the real-time irradiance intensity with the preset irradiance threshold or by analyzing the mutation of the string power generation; the preset irradiance threshold is dynamically adjusted based on the typical irradiance level and seasonal characteristics of the east-west photovoltaic array; When the irradiance significant drop or shadow shielding event is identified, the affected string is automatically cut off; wherein the affected string is the string whose power generation is significantly reduced in the east-west photovoltaic array, and the cutting operation is realized by controlling the switching device at the string level; the switching device at the string level includes a relay or a solid-state switch, which is used to isolate the affected string to reduce system loss; When the light condition recovers, the previously cut string is automatically reconnected; wherein the light condition recovery is determined by monitoring the irradiance intensity in real time and comparing the real-time irradiance intensity with the preset recovery threshold, and the reconnection operation is realized by controlling the switching device at the string level; the preset recovery threshold is set based on the output of the short-term irradiance prediction algorithm and the historical performance data of the east-west photovoltaic array; Based on the dynamic string switching module, the overall power generation efficiency of the east-west photovoltaic array is optimized; wherein the dynamic string switching module adjusts the string connection state to maintain high-efficiency power generation of the photovoltaic array under irradiance change and shadow shielding condition, and cooperates with the power generation-price coupling model to ensure that the power generation curve matches the peak-valley electricity price period.

7. The method of claim 1, wherein the method further comprises: The power generation-price coupling model is updated regularly to reflect the changes of electricity price policy and the performance degradation of photovoltaic array, and the sensitivity analysis is performed to evaluate the impact of the narrowing of peak-valley price difference on power generation income, which includes the following steps: periodically triggering a model updating process; wherein the periodic triggering is based on a preset updating period or an external event, the preset updating period is monthly, quarterly or annually, and the external event includes a power price policy publishing event or a photovoltaic array performance detection report generating event; obtaining updated power price policy information and photovoltaic array performance attenuation data; wherein the updated power price policy information is obtained from a local power grid operator or a power market platform; and the photovoltaic array performance attenuation data is obtained based on analysis of power generation data in a monitoring database and photovoltaic component performance parameters, including component efficiency attenuation rate and temperature coefficient change; based on the updated power price policy information and photovoltaic array performance attenuation data, performing a basic update on the power generation-price coupling model to generate a benchmark power distribution strategy and a benchmark inverter operating parameter; wherein the basic update includes updating the power price curve and photovoltaic component performance parameters in the power generation-price coupling model. 8.The method of claim 7, wherein, periodically updating the power generation-price coupling model to reflect changes in power price policy and photovoltaic array performance attenuation, and performing a sensitivity analysis to evaluate the impact of peak-valley price difference narrowing on power generation revenue; further comprising the following steps: performing a sensitivity analysis to evaluate the impact of peak-valley price difference narrowing on power generation revenue; wherein the sensitivity analysis calculates a revenue sensitivity index by simulating changes in power generation revenue under different peak-valley price difference scenarios, and the revenue sensitivity index is an elasticity coefficient of power generation revenue with respect to peak-valley price difference; based on the revenue sensitivity index obtained from the sensitivity analysis, performing a defensive optimization on the benchmark power distribution strategy and the benchmark inverter operating parameter to generate a final applied power distribution strategy and inverter operating parameter; wherein the defensive optimization includes increasing the power output target value of the benchmark power distribution strategy during peak hours, or adjusting the inverter output power limit in the benchmark inverter operating parameter, to mitigate the negative impact of peak-valley price difference narrowing on power generation revenue. 9.The method of claim 1, wherein, The east-west asymmetric layout feature of the photovoltaic array specifically includes: The east-west photovoltaic array is composed of power generation components, and the power generation components are double-sided power generation components or single-sided single-crystal silicon components.

10. The method of claim 9, wherein the method further comprises: The east-west asymmetric layout feature of the photovoltaic array specifically further includes: The capacity ratio range of the east-side photovoltaic array is 1.3-1.5, and the capacity ratio range of the west-side photovoltaic array is 1.1-1.3.

Images