Photovoltaic power generation energy storage optimization system, method and device

Abstract

The invention discloses a photovoltaic power generation energy storage optimization system, method and device, and belongs to the technical field of photovoltaic power generation. The method comprises the following steps: deploying a photovoltaic array comprising a plurality of photovoltaic cell panels, connecting the photovoltaic array with an electric energy management system through a connecting line, and adjusting the inclination angle of the cell panels according to a real-time illumination angle; configuring an energy storage device comprising a plurality of battery packs connected in parallel and a battery management system, wherein the battery packs are connected with an electric energy management system through a bidirectional DC / DC converter; through illumination, power and environment temperature and humidity sensors, power generation and energy storage data such as photovoltaic array power generation power, illumination intensity and temperature and humidity of an area where the photovoltaic array is located are collected; determining an optimization scheme based on the data, wherein the optimization scheme covers an energy matching relation between photovoltaic power generation and energy storage, a photovoltaic power generation priority supply strategy and an energy storage charging and discharging power adjustment parameter; the energy flow is optimized according to the scheme. According to the invention, illumination fluctuation and load requirements are efficiently adapted, dynamic balance of photovoltaic power generation and energy storage is realized, and the energy utilization efficiency and the power supply stability are improved.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power generation technology, and more specifically to photovoltaic power generation energy storage optimization systems, methods and devices. Background Technology

[0002] With the rapid development of solar power generation technology, photovoltaic power generation, as an environmentally friendly and renewable energy form, has gradually received widespread attention. However, due to the instability of solar energy resources and the volatility of the electricity market, how to effectively integrate photovoltaic power generation and energy storage technologies to achieve efficient energy utilization and economy has become a key challenge in the field.

[0003] Traditional photovoltaic (PV) power generation systems often only generate electricity when solar energy is abundant, failing to supply power at night or on cloudy days, resulting in instability. Furthermore, the periodic fluctuations in electricity prices in the power market also affect energy purchase and supply strategies. Therefore, there is a need in this field for a highly efficient PV power generation and energy storage optimization system. This system should be able to intelligently adjust energy allocation strategies based on the real-time status of PV power generation and energy storage devices, as well as real-time electricity price changes in the power market, to maximize energy utilization efficiency and reduce electricity purchase costs. Summary of the Invention

[0004] The purpose of this invention is to provide a photovoltaic power generation energy storage optimization system, method, and apparatus to solve the following technical problems: Traditional photovoltaic power generation systems often only generate electricity when there is sufficient solar energy, and cannot supply power at night or on cloudy days, resulting in instability. At the same time, the periodic fluctuations in electricity prices in the electricity market also affect energy purchasing and supply strategies.

[0005] The objective of this invention can be achieved through the following technical solutions: Photovoltaic power generation and energy storage optimization system, including: A photovoltaic array is an arrangement of multiple photovoltaic panels used to convert solar radiation into electrical energy. The photovoltaic array is connected to a power management system via connecting lines. The photovoltaic array is used to adjust the tilt angle of the panels according to the real-time sunlight angle. Energy storage device: A device for storing electrical energy generated by photovoltaic power generation. The energy storage device includes multiple parallel battery packs and a battery management system. The battery packs are connected to the energy management system through a bidirectional DC / DC converter. The energy storage device is used to regulate power according to charge and discharge commands. The data acquisition module is used to acquire power generation and energy storage data of the photovoltaic array through a light sensor, a power sensor, and an ambient temperature and humidity sensor; the power generation and energy storage data includes: the power generation of the photovoltaic array, the light intensity of the area where the photovoltaic array is located, and the ambient temperature and humidity data; The optimization scheme determination module is used to determine the photovoltaic power generation and energy storage optimization scheme based on the power generation and energy storage data of the photovoltaic array; the photovoltaic power generation and energy storage optimization scheme includes: the energy matching relationship between photovoltaic power generation and energy storage, the photovoltaic power generation priority supply strategy, and the energy storage charging and discharging power adjustment parameters; The power management module is used to optimize the energy flow between photovoltaic power generation and energy storage devices according to the photovoltaic power generation and energy storage optimization scheme. The power management system includes sensors, control units, data processing units, and optimization units.

[0006] As a further aspect of the present invention: the optimization unit calculates the energy matching relationship between photovoltaic power generation and energy storage based on the following formula: E-matching = α·P photovoltaic - β·P load + γE energy storage; Where Ematching is the energy matching value, α is the weighting coefficient between photovoltaic power generation and energy matching, Pphotovoltaic is the current power of photovoltaic power generation; β is the weighting coefficient between load and energy matching, Pload is the current power of load; γ is the weighting coefficient between energy storage and energy matching; and E is the current energy storage capacity of the energy storage device.

[0007] As a further aspect of the present invention, the optimization unit further includes the following calculation formula: Plight(t) = Ppredict(t) - Ppeak-clipping(t); Pstorage(t) = Pdemand(t) - Plight(t); Wherein, Plight(t) is the power generation at time t, Ppredicted(t) is the predicted power generation, Ppeak shaving(t) is the peak power shaving adjusted according to the peak-valley balance, Pstorage(t) is the energy storage charging and discharging power at time t, and Pdemand(t) is the predicted power demand. P forecast (t) and P demand (t) are calculated based on historical data and power consumption patterns, while P peak shaving (t) is dynamically adjusted based on grid load conditions.

[0008] As a further aspect of the present invention, it also includes a database for storing records of the power generation and light intensity of the photovoltaic array, generating historical power generation change database and historical light intensity change database respectively; and obtaining a future time t light intensity database through a meteorological center.

[0009] As a further aspect of the present invention, it also includes a power prediction module, which is used to extract data on the change of light intensity over time within a future time period t, and to fit and generate a light intensity-time curve q1. Based on the historical light intensity change database, a historical light intensity change curve is generated. The curve segment with the highest similarity to the historical light intensity change curve q1 is extracted from the historical light intensity change curve and named the curve segment as the historical light intensity change curve q2. Therefore, the historical power generation change database corresponding to the time period of the illumination-time curve q2 is extracted, and the power generation data of this time period is marked as P prediction(t).

[0010] As a further aspect of the present invention: the power management system has remote monitoring and control functions, and communicates with a remote server through a communication network to realize remote monitoring, fault diagnosis and parameter adjustment of the system.

[0011] As a further aspect of the present invention, it also includes a safety protection module for monitoring and protecting the operational safety of the system, including overvoltage, overcurrent, and overtemperature protection functions.

[0012] As a further aspect of the present invention: the optimization unit obtains real-time electricity price data from the electricity market by interacting with the electricity market. The real-time electricity price data covers the price changes in different time periods, including peak and off-peak periods. Based on the real-time electricity price data, when the electricity price is high, the α weighting coefficient is appropriately increased to enhance the advantages of photovoltaic power generation; when the electricity price is low, the β and γ weighting coefficients are appropriately increased to emphasize the advantages of energy storage charging and discharging. During periods when photovoltaic power generation capacity is sufficient and electricity prices are high, the optimization unit ensures that photovoltaic power generation prioritizes supplying electricity demand and stores excess electricity in energy storage devices to maximize the utilization of electricity. During periods of low electricity prices, the optimization unit prioritizes energy storage charging and discharging, purchasing and storing electrical energy.

[0013] The photovoltaic power generation and energy storage optimization method includes the following steps: A photovoltaic array consisting of multiple photovoltaic panels connected in series and parallel is deployed; the photovoltaic array is connected to a power management system via connecting lines; the photovoltaic array is used to adjust the tilt angle of the panels according to the real-time illumination angle. An energy storage device is configured; the energy storage device includes multiple parallel battery packs and a battery management system; the battery packs are connected to the energy management system via a bidirectional DC / DC converter; wherein the energy storage device is used to regulate power according to charge and discharge commands. The photovoltaic array's power generation and energy storage data are acquired using a light sensor, a power sensor, and an ambient temperature and humidity sensor. The power generation and energy storage data includes: the photovoltaic array's power generation, the light intensity of the area where the photovoltaic array is located, and the ambient temperature and humidity data. Based on the power generation and energy storage data of the photovoltaic array, an optimized photovoltaic power generation and energy storage scheme is determined; the optimized photovoltaic power generation and energy storage scheme includes: the energy matching relationship between photovoltaic power generation and energy storage, the photovoltaic power generation priority supply strategy, and the energy storage charging and discharging power adjustment parameters; According to the photovoltaic power generation and energy storage optimization scheme, the energy flow between photovoltaic power generation and energy storage devices is optimized; the power management system includes sensors, control units, data processing units and optimization units.

[0014] As a further aspect of the present invention: the energy matching relationship between photovoltaic power generation and energy storage is calculated based on the following formula: E-matching = α·P photovoltaic - β·P load + γE energy storage Where Ematching is the energy matching value, α is the weighting coefficient between photovoltaic power generation and energy matching, Pphotovoltaic is the current power of photovoltaic power generation; β is the weighting coefficient between load and energy matching, Pload is the current power of load; γ is the weighting coefficient between energy storage and energy matching; and E is the current energy storage capacity of the energy storage device.

[0015] As a further aspect of the present invention, the following calculation formula is also included: Plight(t) = Ppredict(t) - Ppeak-clipping(t); Pstorage(t) = Pdemand(t) - Plight(t); Wherein, Plight(t) is the power generation at time t, Ppredicted(t) is the predicted power generation, Ppeak shaving(t) is the peak power shaving adjusted according to the peak-valley balance, Pstorage(t) is the energy storage charging and discharging power at time t, and Pdemand(t) is the predicted power demand. P forecast (t) and P demand (t) are calculated based on historical data and power consumption patterns, while P peak shaving (t) is dynamically adjusted based on grid load conditions.

[0016] As a further aspect of the present invention: a database is constructed to store the power generation and light intensity records of the photovoltaic array, and a historical power generation change database and a historical light intensity change database are generated respectively; and a future time t light intensity database is obtained through a meteorological center.

[0017] As a further aspect of the present invention: extract the data on the change of light intensity over time within a future time period t, and fit and generate a light intensity curve q1 over time; Based on the historical light intensity change database, a historical light intensity change curve is generated. The curve segment with the highest similarity to the historical light intensity change curve q1 is extracted from the historical light intensity change curve and named the curve segment as the historical light intensity change curve q2. Therefore, the historical power generation change database corresponding to the time period of the illumination-time curve q2 is extracted, and the power generation data of this time period is marked as P prediction(t).

[0018] As a further aspect of the present invention: communication with a remote server via a communication network enables remote monitoring, fault diagnosis, and parameter adjustment of the system.

[0019] As a further aspect of the present invention, it also includes a safety protection module for monitoring and protecting the operational safety of the system, including overvoltage, overcurrent, and overtemperature protection functions.

[0020] As a further aspect of the present invention: the optimization unit obtains real-time electricity price data from the electricity market by interacting with the electricity market. The real-time electricity price data covers the price changes in different time periods, including peak and off-peak periods. Based on the real-time electricity price data, when the electricity price is high, the α weighting coefficient is appropriately increased to enhance the advantages of photovoltaic power generation; when the electricity price is low, the β and γ weighting coefficients are appropriately increased to emphasize the advantages of energy storage charging and discharging. During periods when photovoltaic power generation capacity is sufficient and electricity prices are high, the optimization unit ensures that photovoltaic power generation prioritizes supplying electricity demand and stores excess electricity in energy storage devices to maximize the utilization of electricity. During periods of low electricity prices, the optimization unit prioritizes energy storage charging and discharging, purchasing and storing electrical energy.

[0021] Photovoltaic power generation and energy storage optimization device, including: A photovoltaic array is an arrangement of multiple photovoltaic panels used to convert solar radiation into electrical energy. The photovoltaic array is connected to a power management system via connecting lines. The photovoltaic array is used to adjust the tilt angle of the panels according to the real-time sunlight angle. Energy storage device: A device for storing electrical energy generated by photovoltaic power generation. The energy storage device includes multiple parallel battery packs and a battery management system. The battery packs are connected to the energy management system through a bidirectional DC / DC converter. The energy storage device is used to regulate power according to charge and discharge commands. The data acquisition module is used to acquire power generation and energy storage data of the photovoltaic array through a light sensor, a power sensor, and an ambient temperature and humidity sensor; the power generation and energy storage data includes: the power generation of the photovoltaic array, the light intensity of the area where the photovoltaic array is located, and the ambient temperature and humidity data; The optimization scheme determination module is used to determine the photovoltaic power generation and energy storage optimization scheme based on the power generation and energy storage data of the photovoltaic array; the photovoltaic power generation and energy storage optimization scheme includes: the energy matching relationship between photovoltaic power generation and energy storage, the photovoltaic power generation priority supply strategy, and the energy storage charging and discharging power adjustment parameters; The power management module is used to optimize the energy flow between photovoltaic power generation and energy storage devices according to the photovoltaic power generation and energy storage optimization scheme. The power management system includes sensors, control units, data processing units, and optimization units.

[0022] As a further aspect of the present invention: the optimization unit calculates the energy matching relationship between photovoltaic power generation and energy storage based on the following formula: E-matching = α·P photovoltaic - β·P load + γE energy storage; Where Ematching is the energy matching value, α is the weighting coefficient between photovoltaic power generation and energy matching, Pphotovoltaic is the current power of photovoltaic power generation; β is the weighting coefficient between load and energy matching, Pload is the current power of load; γ is the weighting coefficient between energy storage and energy matching; and E is the current energy storage capacity of the energy storage device.

[0023] As a further aspect of the present invention, the optimization unit further includes the following calculation formula: Plight(t) = Ppredict(t) - Ppeak-clipping(t); Pstorage(t) = Pdemand(t) - Plight(t); Wherein, Plight(t) is the power generation at time t, Ppredicted(t) is the predicted power generation, Ppeak shaving(t) is the peak power shaving adjusted according to the peak-valley balance, Pstorage(t) is the energy storage charging and discharging power at time t, and Pdemand(t) is the predicted power demand. P forecast (t) and P demand (t) are calculated based on historical data and power consumption patterns, while P peak shaving (t) is dynamically adjusted based on grid load conditions.

[0024] As a further aspect of the present invention, it also includes a database for storing records of the power generation and light intensity of the photovoltaic array, generating historical power generation change database and historical light intensity change database respectively; and obtaining a future time t light intensity database through a meteorological center.

[0025] As a further aspect of the present invention, it also includes a power prediction module, which is used to extract data on the change of light intensity over time within a future time period t, and to fit and generate a light intensity-time curve q1. Based on the historical light intensity change database, a historical light intensity change curve is generated. The curve segment with the highest similarity to the historical light intensity change curve q1 is extracted from the historical light intensity change curve and named the curve segment as the historical light intensity change curve q2. Therefore, the historical power generation change database corresponding to the time period of the illumination-time curve q2 is extracted, and the power generation data of this time period is marked as P prediction(t).

[0026] As a further aspect of the present invention: the power management system has remote monitoring and control functions, and communicates with a remote server through a communication network to realize remote monitoring, fault diagnosis and parameter adjustment of the system.

[0027] As a further aspect of the present invention, it also includes a safety protection module for monitoring and protecting the operational safety of the system, including overvoltage, overcurrent, and overtemperature protection functions.

[0028] As a further aspect of the present invention: the optimization unit obtains real-time electricity price data from the electricity market by interacting with the electricity market. The real-time electricity price data covers the price changes in different time periods, including peak and off-peak periods. Based on the real-time electricity price data, when the electricity price is high, the α weighting coefficient is appropriately increased to enhance the advantages of photovoltaic power generation; when the electricity price is low, the β and γ weighting coefficients are appropriately increased to emphasize the advantages of energy storage charging and discharging. During periods when photovoltaic power generation capacity is sufficient and electricity prices are high, the optimization unit ensures that photovoltaic power generation prioritizes supplying electricity demand and stores excess electricity in energy storage devices to maximize the utilization of electricity. During periods of low electricity prices, the optimization unit prioritizes energy storage charging and discharging, purchasing and storing electrical energy.

[0029] The beneficial effects of this invention are: This invention optimizes the energy matching calculation of the unit, enabling the system to intelligently allocate photovoltaic power generation and energy storage charging and discharging according to power demand, maximizing the utilization of solar energy resources and the storage and release of electrical energy, thereby improving overall energy efficiency. By monitoring the photovoltaic power generation and energy storage device status in real time, the system can flexibly adjust the energy allocation strategy according to the actual energy situation, ensuring the stability and continuity of power supply. The power prediction module combines historical data and power consumption patterns, and through the analysis of data on changes in light intensity over time, predicts future photovoltaic power generation and power demand, enabling the system to make reasonable energy allocation arrangements in advance. Attached Figure Description

[0030] The invention will now be further described with reference to the accompanying drawings.

[0031] Figure 1 This is a flowchart illustrating the present invention. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] Please see Figure 1 As shown, the present invention relates to a photovoltaic power generation energy storage optimization system, method, and apparatus, comprising: Step 101: Deploy a photovoltaic array consisting of multiple photovoltaic panels connected in series and parallel; the photovoltaic array is connected to the power management system via connecting lines; wherein the photovoltaic array is used to adjust the tilt angle of the panels according to the real-time illumination angle; Step 102: Configure an energy storage device; the energy storage device includes multiple parallel battery packs and a battery management system; the battery packs are connected to the energy management system via a bidirectional DC / DC converter; the energy storage device is used to regulate power according to charge and discharge commands; Step 103: Obtain power generation and energy storage data of the photovoltaic array through a light sensor, a power sensor, and an ambient temperature and humidity sensor; the power generation and energy storage data includes: photovoltaic array power generation, light intensity of the area where the photovoltaic array is located, and ambient temperature and humidity data; Step 104: Based on the power generation and energy storage data of the photovoltaic array, determine the photovoltaic power generation and energy storage optimization scheme; the photovoltaic power generation and energy storage optimization scheme includes: the energy matching relationship between photovoltaic power generation and energy storage, the photovoltaic power generation priority supply strategy, and the energy storage charging and discharging power adjustment parameters; Step 105: Optimize the energy flow between photovoltaic power generation and energy storage devices according to the photovoltaic power generation and energy storage optimization scheme; the power management system includes sensors, control units, data processing units and optimization units.

[0034] In step 101, a photovoltaic array consisting of multiple photovoltaic panels connected in series and parallel is deployed; the photovoltaic array is connected to the power management system via connecting lines; wherein the photovoltaic array is used to adjust the tilt angle of the panels according to the real-time illumination angle.

[0035] In a specific embodiment, the deployment and tilt adjustment of the photovoltaic array are designed to meet the needs of efficient utilization of solar energy resources, specifically: During implementation, the photovoltaic array adopts a modular structure with multiple series and parallel connections. Each group contains multiple high-efficiency monocrystalline silicon photovoltaic panels, which are connected in series to control the rated voltage and adapt to the input voltage range of the power management system. Each array is equipped with an independent DC combiner box, which has built-in reverse connection protection diodes, fuses, and surge protectors to prevent the failure of a single panel from affecting the operation of the entire array. The combiner box communicates with the power management system through shielded connection lines. The lines are made of flame-retardant and waterproof materials, and redundant lengths are reserved during installation to accommodate the line stretching requirements during tilt angle adjustments.

[0036] Each photovoltaic array module is equipped with an electric tilt adjustment device, including a stepper motor, an angle sensor, a reduction gear set, and a controller. The stepper motor adopts a high-precision closed-loop control model; the angle sensor collects the current tilt angle of the solar panel in real time, and the data is transmitted to the power management system via bus; the reduction gear set has a self-locking function to ensure that the solar panel is stably fixed after tilt adjustment and resists external interference such as gusts of wind.

[0037] Dynamic adjustment logic based on real-time illumination angle: The tilt adjustment strategy is led by the optimization unit of the power management system, and control is achieved by combining multi-dimensional data. Illumination Angle Acquisition: Real-time solar altitude angle and azimuth angle are collected by the illumination sensor of the data acquisition module. At the same time, combined with the latitude and longitude, date and time of the area where the photovoltaic array is located, the theoretically optimal tilt angle is calculated by astronomical algorithms. Adjustment rules: When the deviation between the real-time illumination angle and the current solar panel tilt angle exceeds 5°, a tilt angle adjustment command is triggered; during the morning period, the azimuth angle is adjusted first to adapt to the sun's eastward trajectory, and during the afternoon period, the elevation angle is adjusted to improve the efficiency of direct sunlight reception; on cloudy days or when the illumination intensity is lower than the preset threshold, the preset reference tilt angle is maintained to avoid frequent adjustments that would cause energy waste. The tilt adjustment device has overload protection. When the motor load exceeds 1.2 times the rated value, it automatically stops and issues an alarm. If the wind speed exceeds the preset value during adjustment, the adjustment is immediately paused, and the solar panel is adjusted to the minimum stress angle on the windward side to prevent structural damage. At the same time, the optimization unit calculates the energy consumption and power generation improvement benefits of tilt adjustment in real time. When the estimated benefit is lower than the energy cost, the adjustment operation is delayed to achieve a balance between energy consumption and benefit.

[0038] Linkage with power generation: The power management system monitors the power generation of the photovoltaic array in real time. If the increase in power generation is less than the preset threshold within a preset time after adjusting the tilt angle, the tilt angle is finely adjusted in the opposite direction and monitoring is repeated. Through the process of adjustment, monitoring and calibration, the tilt angle is always kept in the optimal state.

[0039] In step 102, an energy storage device is configured; the energy storage device includes multiple parallel battery packs and a battery management system; the battery packs are connected to the energy management system through a bidirectional DC / DC converter; wherein the energy storage device is used to regulate power according to charge and discharge commands.

[0040] In a specific embodiment, the configuration and power regulation mechanism of the energy storage device are designed around the volatility adaptation and safe and stable operation of photovoltaic power generation, specifically: The energy storage device adopts a modular structure with multiple lithium battery packs connected in parallel. Each battery pack consists of multiple lithium iron phosphate power batteries connected in series, which has the characteristics of high safety and long life.

[0041] Each battery pack is independently equipped with a battery information acquisition module to monitor the voltage and temperature of individual cells and the total current of the module in real time. The data is uploaded to the battery management system in real time via the bus, providing accurate data for charge and discharge control.

[0042] The parallel structure design supports hot-swapping between groups, and can quickly disconnect from the system when a single battery fails without affecting the operation of the overall energy storage device. At the same time, it reserves multiple expansion interfaces to adapt to future photovoltaic power generation capacity expansion needs.

[0043] Each battery pack corresponds to an independent bidirectional DC / DC converter, which adopts a full-bridge topology to meet the power regulation requirements of medium and high power scenarios.

[0044] Battery Management System (BMS): Condition monitoring and assessment: The BMS calculates the SOC of each battery pack in real time, such as the state of charge, state of health, and state of energy. Based on the ampere-hour integration method combined with open-circuit voltage calibration, it avoids SOC drift. Through the single-cell voltage equalization algorithm, when the voltage difference between single cells exceeds the preset threshold, it automatically starts passive equalization to ensure the consistency of the battery pack.

[0045] Charge and discharge strategy control: Based on the charge and discharge instructions issued by the power management system and combined with the real-time status of the battery pack, the charging and discharging parameters are dynamically adjusted: During charging, a constant current and constant voltage two-stage charging strategy is adopted to avoid overcharging; during discharging, a constant power discharging strategy is adopted to limit the maximum discharge current and extend battery life; when the SOC is lower than 10% or higher than 90%, the charging and discharging power is automatically adjusted to trigger low / high SOC warnings.

[0046] Safety protection mechanism: The BMS has built-in safety protection logic, including overvoltage, overcurrent, overtemperature and short circuit protection. After the protection is triggered, the charging and discharging circuit of the corresponding battery pack is immediately cut off and an alarm signal is sent to the power management system. At the same time, fault information is recorded, including timestamp, fault type and battery status, for subsequent traceability.

[0047] In step 103, the power generation and energy storage data of the photovoltaic array are obtained through a light sensor, a power sensor, and an ambient temperature and humidity sensor; the power generation and energy storage data includes: the power generation of the photovoltaic array, the light intensity of the area where the photovoltaic array is located, and the ambient temperature and humidity data.

[0048] In a specific embodiment, the data acquisition mechanism for power generation and energy storage is designed around high precision, high reliability, and high synchronization, adapting to the needs of photovoltaic power generation fluctuation monitoring and energy storage optimization. Specifically: Sensor selection: Light sensor: Employing a digital silicon photovoltaic cell sensor, it can accurately capture sudden changes in light intensity under conditions such as cloud cover, sunrise, and sunset. The sensor is deployed on top of the support structure in the central area of ​​the photovoltaic array, parallel to the light-receiving surface of the solar panels, and is equipped with an anti-glare shield to avoid measurement errors caused by rain, dust, and strong light reflection.

[0049] Power sensor: A Hall effect DC power sensor is selected. The sensor is connected in series in the main circuit between the photovoltaic array combiner box and the power management system. It has a built-in temperature compensation module to offset the impact of ambient temperature changes on measurement accuracy and ensure that the power generation data is true and reliable.

[0050] Ambient temperature and humidity sensor: An industrial-grade integrated temperature and humidity sensor is used. The sensor is deployed at the edge of the photovoltaic array in a ventilated area to avoid interference from the heat dissipation of the solar panel on the temperature measurement. At the same time, the waterproof and sealed design is suitable for harsh outdoor environments.

[0051] Data collection: All sensors communicate with the data acquisition module via a bus. The data frame contains the sensor address, data type, measurement value, and checksum, ensuring that there is no packet loss or tampering during transmission.

[0052] The data acquisition frequency is set according to the principle of hierarchical adaptation: light intensity and power generation are high-frequency acquisition items (1 time / 10s) to adapt to the rapid fluctuation characteristics of photovoltaic power generation; ambient temperature and humidity are low-frequency acquisition items (1 time / 60s) to balance data validity and communication bandwidth saving.

[0053] A time synchronization mechanism is introduced: using the GPS timing module of the power management system as a reference, all sensors are calibrated once per hour to ensure that the timestamp deviation of data collected by different sensors is less than a preset threshold, thus providing time consistency assurance for subsequent correlation analysis of illumination, power, temperature and humidity.

[0054] Data preprocessing: The collected data is preprocessed by the data acquisition module before being transmitted to the power management system: the power generation data is filtered by moving average to remove instantaneous pulse interference; the light intensity data is filtered by threshold, and when the deviation of a single measurement value from the average of the previous three measurements exceeds the preset threshold, it is judged as an abnormal value and is completed by linear interpolation; the temperature and humidity data are checked for range, and data that exceeds the sensor's measurement range is marked as invalid and triggers the sensor's self-test.

[0055] Data storage: The preprocessed power generation and energy storage data is stored in a structured format of timestamp-sensor ID-photovoltaic array partition-measurement value, with data status marked as "normal / interpolated / alternate" to facilitate subsequent traceability of data source reliability.

[0056] Establish a data association mapping table: bind the data of light intensity, ambient temperature and humidity with the power generation data of the corresponding time period, and record the real-time tilt angle of the photovoltaic array and the SOC status of the energy storage device, so as to provide complete data support for the modeling of the light-power relationship of the power prediction module and the energy matching calculation of the optimization unit.

[0057] In step 104, based on the power generation and energy storage data of the photovoltaic array, a photovoltaic power generation and energy storage optimization scheme is determined; the photovoltaic power generation and energy storage optimization scheme includes: the energy matching relationship between photovoltaic power generation and energy storage, the photovoltaic power generation priority supply strategy, and the energy storage charging and discharging power adjustment parameters.

[0058] In a specific embodiment, the determination of the photovoltaic power generation and energy storage optimization scheme combines real-time power generation and energy storage data, power prediction results, and electricity market price signals to form an optimization strategy, specifically: Calculation of energy matching relationship: Based on the energy matching formula built into the optimization unit: E-matching = α·P photovoltaic - β·P load + γE energy storage Where Ematching is the energy matching value, α is the weighting coefficient between photovoltaic power generation and energy matching, Pphotovoltaic is the current power of photovoltaic power generation; β is the weighting coefficient between load and energy matching, Pload is the current power of load; γ is the weighting coefficient between energy storage and energy matching; and E is the current energy storage capacity of the energy storage device.

[0059] The weighting coefficients are dynamically adjusted: the initial values ​​of α, β, and γ are set according to the basic operating conditions (α=0.4, β=0.3, γ=0.3), and then dynamically calibrated according to real-time light intensity, energy storage SOC, and electricity price. For example, when the light intensity is >100,000 lux, α is increased to 0.5-0.6; when the energy storage SOC is <30%, γ is increased to 0.4-0.5; during high electricity price periods (such as 10:00-14:00), α is increased to 0.55; during low electricity price periods (such as 00:00-06:00), β and γ are increased to 0.35 and 0.4, respectively.

[0060] Matching result classification: When E matching > 0.2 (energy surplus), the energy storage charging strategy is triggered; when -0.2 ≤ E matching ≤ 0.2 (energy balance), the current operating state is maintained; when E matching < -0.2 (energy deficit), the energy storage discharge or grid power purchase supplementation strategy is triggered.

[0061] Priority supply strategy for photovoltaic power generation: Basic supply logic: Photovoltaic power generation prioritizes directly meeting current load demand. That is, when P_photovoltaic ≤ P_load, photovoltaic power supplies the load in full, and energy storage is kept on standby or charged and discharged according to the electricity price strategy. When P_photovoltaic > P_load, excess electricity is stored in energy storage devices first. After the energy storage is fully charged, peak shaving or grid connection strategies are triggered.

[0062] Scene optimization: In scenarios with stable sunlight (sunlight intensity fluctuation ≤5% / min): photovoltaic power follows load demand in real time to ensure power supply stability; In scenarios of sudden changes in sunlight (sunlight intensity fluctuation > 10% / min): the photovoltaic power is output smoothly according to the average value of the previous 3 seconds, and the energy storage responds quickly and synchronously to offset the impact of photovoltaic fluctuations on the load; In high-electricity-price scenarios: Solar power should be prioritized for sensitive loads, such as precision equipment and residential electricity consumption. Large industrial loads can be partially switched to energy storage power supply to maximize the benefits of solar power. Grid-constrained scenarios: Photovoltaic power is output according to the curtailment threshold, and excess electricity is stored in energy storage to avoid curtailment.

[0063] Energy storage charging and discharging power adjustment parameters: Charging power adjustment: Based on the formula: Among them, P storage max The maximum charging power for energy storage is ηcharge (SOC). ηcharge is the SOC-adapted charging efficiency coefficient. Specifically, when SOC < 30%, ηcharge = 0.95 (full power charging); when 30% ≤ SOC ≤ 80%, ηcharge = 0.98 (optimal efficiency charging); and when SOC > 80%, ηcharge = 0.8 (reduced power charging) to avoid overcharging and damaging the battery.

[0064] Discharge power adjustment: Based on the formula: Among them, P storage max The maximum discharge power of the energy storage is η_discharge(SOC), which is the SOC-adaptive discharge efficiency coefficient. That is, when SOC > 20%, η_discharge = 0.95 (normal discharge); when 10% ≤ SOC ≤ 20%, η_discharge = 0.8 (reduced power discharge); when SOC < 10%, discharge stops to protect the battery.

[0065] In step 105, the energy flow between the photovoltaic power generation and energy storage devices is optimized according to the photovoltaic power generation and energy storage optimization scheme; the power management system includes sensors, control units, data processing units and optimization units.

[0066] In specific embodiments, based on the optimization of energy flow in the photovoltaic power generation energy storage optimization scheme, through the collaborative linkage of each unit of the power management system, optimization control is achieved. Specifically: Sensor real-time feedback: A sensor group distributed in the photovoltaic array, energy storage device, and transmission line collects data such as photovoltaic output voltage and current, energy storage SOC and temperature, and line power loss in real time, and transmits it to the data processing unit to provide a basis for real-time operating conditions for regulation.

[0067] Data processing unit preprocessing: Filter, calibrate, and perform correlation analysis on the collected data, eliminate instantaneous interference signals, and at the same time combine historical data and power prediction results in the database to generate operating conditions, demands, optimization goals, and data reports, and output them to the optimization unit.

[0068] Optimization unit dynamic decision-making: Based on the energy matching formula E matching = α·P photovoltaic - β·P load + γE energy storage and the charge and discharge power calculation formula, combined with real-time electricity prices and grid constraints, dynamically generate energy distribution instructions, and clarify the photovoltaic power supply ratio, energy storage charge and discharge power, and grid interaction strategy.

[0069] Control unit execution: Receive the optimization instruction, and adjust the output of the controller of the photovoltaic array and the working state of the bidirectional DC / DC converter of the energy storage device through pulse width modulation technology to ensure that the energy flow is accurately executed according to the optimization scheme.

[0070] Scene-based optimization strategy for energy flow Scenario of sufficient sunlight and peak electricity price (P photovoltaic > P load, electricity price ≥ 1.2 times the flat-section electricity price): The photovoltaic array preferentially supplies the load in full, and the excess electricity adjusts the charging power dynamically according to the current SOC of the energy storage device. That is, when SOC < 80%, the excess electricity is stored with the maximum charging power (50kW); when SOC ≥ 80%, the peak shaving strategy is started, and the part of the electricity exceeding the load demand and the energy storage storage capacity is smoothly fed into the grid according to the grid acceptance threshold to avoid light abandonment.

[0071] The control unit monitors the grid voltage fluctuation in real time. When the voltage deviation exceeds the preset threshold, the fluctuation is suppressed by the rapid charge and discharge of the energy storage device to ensure that the quality of the electricity fed into the grid meets the standards.

[0072] Scenario of insufficient sunlight and low electricity price (P photovoltaic < P load, electricity price ≤ 0.8 times the flat-section electricity price): The photovoltaic supplies the load in full, and the gap is preferentially supplemented by the discharge of the energy storage device. The discharge power is calculated according to P storage discharge(t) = P load(t) - P photovoltaic(t), and does not exceed the maximum discharge power of the energy storage and the SOC adaptation threshold (SOC ≥ 10%).

[0073] When the energy storage SOC is less than 10%, it automatically switches to grid power purchase mode, and the purchased power increases smoothly to avoid the impact of sudden power changes on the load. At the same time, the purchased power data is recorded for subsequent cost accounting.

[0074] Lighting fluctuation scene: The photovoltaic power output is stable based on the average value of the previous preset number of samplings. The energy storage device tracks the power gap in real time and offsets the photovoltaic fluctuations through rapid charging and discharging, ensuring that the power supply fluctuation on the load side is less than the preset threshold.

[0075] The data processing unit analyzes the fluctuation trend in real time. If the fluctuation continues, the optimization unit automatically adjusts the weighting coefficients of α and β (α is lowered to 0.3 and β is raised to 0.4) to enhance the fluctuation suppression effect of energy storage.

[0076] Power grid constraint scenarios: The optimization unit adjusts the photovoltaic output power P_photon(t) = P_predict(t) - P_peakstripping(t) according to the power rationing threshold, where P_peakstripping(t) is dynamically calculated according to the power rationing requirements of the grid to ensure that the photovoltaic output does not exceed the power rationing limit.

[0077] Excess electricity is preferentially stored in energy storage devices. Once the energy storage is fully charged, local load transfer is initiated, such as starting controllable loads like backup energy storage heaters, to maximize the utilization of photovoltaic power and reduce curtailment losses.

[0078] In a preferred embodiment of the present invention, the energy matching relationship between photovoltaic power generation and energy storage is calculated based on the following formula: E-matching = α·P photovoltaic - β·P load + γE energy storage Where Ematching is the energy matching value, α is the weighting coefficient between photovoltaic power generation and energy matching, Pphotovoltaic is the current power of photovoltaic power generation; β is the weighting coefficient between load and energy matching, Pload is the current power of load; γ is the weighting coefficient between energy storage and energy matching; and E is the current energy storage capacity of the energy storage device.

[0079] In a preferred embodiment of the present invention, the following calculation formula is also included: Plight(t) = Ppredict(t) - Ppeak-clipping(t); Pstorage(t) = Pdemand(t) - Plight(t); Wherein, Plight(t) is the power generation at time t, Ppredicted(t) is the predicted power generation, Ppeak shaving(t) is the peak power shaving adjusted according to the peak-valley balance, Pstorage(t) is the energy storage charging and discharging power at time t, and Pdemand(t) is the predicted power demand. P forecast (t) and P demand (t) are calculated based on historical data and power consumption patterns, while P peak shaving (t) is dynamically adjusted based on grid load conditions.

[0080] In a preferred embodiment of the present invention, a database is constructed to store the power generation and light intensity records of the photovoltaic array, and a historical power generation change database and a historical light intensity change database are generated respectively; and a future time t light intensity database is obtained through a meteorological center.

[0081] In a preferred embodiment of the present invention, data on the change of light intensity over time within a future time period t are extracted, and a light intensity-time curve q1 is generated by fitting the curve. Based on the historical light intensity change database, a historical light intensity change curve is generated. The curve segment with the highest similarity to the historical light intensity change curve q1 is extracted from the historical light intensity change curve and named the curve segment as the historical light intensity change curve q2. Therefore, the historical power generation change database corresponding to the time period of the illumination-time curve q2 is extracted, and the power generation data of this time period is marked as P prediction(t).

[0082] In a preferred embodiment of the present invention, communication with a remote server via a communication network enables remote monitoring, fault diagnosis, and parameter adjustment of the system.

[0083] In a preferred embodiment of the present invention, a safety protection module is further included for monitoring and protecting the operational safety of the system, including overvoltage, overcurrent, and overtemperature protection functions.

[0084] In a preferred embodiment of the present invention, the optimization unit obtains real-time electricity price data from the electricity market by interacting with the electricity market. The real-time electricity price data covers the price changes in different time periods, including peak and off-peak periods. Based on the real-time electricity price data, when the electricity price is high, the α weighting coefficient is appropriately increased to enhance the advantages of photovoltaic power generation; when the electricity price is low, the β and γ weighting coefficients are appropriately increased to emphasize the advantages of energy storage charging and discharging. During periods when photovoltaic power generation capacity is sufficient and electricity prices are high, the optimization unit ensures that photovoltaic power generation prioritizes supplying electricity demand and stores excess electricity in energy storage devices to maximize the utilization of electricity. During periods of low electricity prices, the optimization unit prioritizes energy storage charging and discharging, purchasing and storing electrical energy.

[0085] Photovoltaic power generation and energy storage optimization system, including: A photovoltaic array is an arrangement of multiple photovoltaic panels used to convert solar radiation into electrical energy. The photovoltaic array is connected to a power management system via connecting lines. The photovoltaic array is used to adjust the tilt angle of the panels according to the real-time sunlight angle. Energy storage device: A device for storing electrical energy generated by photovoltaic power generation. The energy storage device includes multiple parallel battery packs and a battery management system. The battery packs are connected to the energy management system through a bidirectional DC / DC converter. The energy storage device is used to regulate power according to charge and discharge commands. The data acquisition module is used to acquire power generation and energy storage data of the photovoltaic array through a light sensor, a power sensor, and an ambient temperature and humidity sensor; the power generation and energy storage data includes: the power generation of the photovoltaic array, the light intensity of the area where the photovoltaic array is located, and the ambient temperature and humidity data; The optimization scheme determination module is used to determine the photovoltaic power generation and energy storage optimization scheme based on the power generation and energy storage data of the photovoltaic array; the photovoltaic power generation and energy storage optimization scheme includes: the energy matching relationship between photovoltaic power generation and energy storage, the photovoltaic power generation priority supply strategy, and the energy storage charging and discharging power adjustment parameters; The power management module is used to optimize the energy flow between photovoltaic power generation and energy storage devices according to the photovoltaic power generation and energy storage optimization scheme. The power management system includes sensors, control units, data processing units, and optimization units.

[0086] As a further aspect of the present invention: the optimization unit calculates the energy matching relationship between photovoltaic power generation and energy storage based on the following formula: E-matching = α·P photovoltaic - β·P load + γE energy storage; Where Ematching is the energy matching value, α is the weighting coefficient between photovoltaic power generation and energy matching, Pphotovoltaic is the current power of photovoltaic power generation; β is the weighting coefficient between load and energy matching, Pload is the current power of load; γ is the weighting coefficient between energy storage and energy matching; and E is the current energy storage capacity of the energy storage device.

[0087] As a further aspect of the present invention, the optimization unit further includes the following calculation formula: Plight(t) = Ppredict(t) - Ppeak-clipping(t); Pstorage(t) = Pdemand(t) - Plight(t); Wherein, Plight(t) is the power generation at time t, Ppredicted(t) is the predicted power generation, Ppeak shaving(t) is the peak power shaving adjusted according to the peak-valley balance, Pstorage(t) is the energy storage charging and discharging power at time t, and Pdemand(t) is the predicted power demand. P forecast (t) and P demand (t) are calculated based on historical data and power consumption patterns, while P peak shaving (t) is dynamically adjusted based on grid load conditions.

[0088] As a further aspect of the present invention, it also includes a database for storing records of the power generation and light intensity of the photovoltaic array, generating historical power generation change database and historical light intensity change database respectively; and obtaining a future time t light intensity database through a meteorological center.

[0089] As a further aspect of the present invention, it also includes a power prediction module, which is used to extract data on the change of light intensity over time within a future time period t, and to fit and generate a light intensity-time curve q1. Based on the historical light intensity change database, a historical light intensity change curve is generated. The curve segment with the highest similarity to the historical light intensity change curve q1 is extracted from the historical light intensity change curve and named the curve segment as the historical light intensity change curve q2. Therefore, the historical power generation change database corresponding to the time period of the illumination-time curve q2 is extracted, and the power generation data of this time period is marked as P prediction(t).

[0090] As a further aspect of the present invention: the power management system has remote monitoring and control functions, and communicates with a remote server through a communication network to realize remote monitoring, fault diagnosis and parameter adjustment of the system.

[0091] As a further aspect of the present invention, it also includes a safety protection module for monitoring and protecting the operational safety of the system, including overvoltage, overcurrent, and overtemperature protection functions.

[0092] As a further aspect of the present invention: the optimization unit obtains real-time electricity price data from the electricity market by interacting with the electricity market. The real-time electricity price data covers the price changes in different time periods, including peak and off-peak periods. Based on the real-time electricity price data, when the electricity price is high, the α weighting coefficient is appropriately increased to enhance the advantages of photovoltaic power generation; when the electricity price is low, the β and γ weighting coefficients are appropriately increased to emphasize the advantages of energy storage charging and discharging. During periods when photovoltaic power generation capacity is sufficient and electricity prices are high, the optimization unit ensures that photovoltaic power generation prioritizes supplying electricity demand and stores excess electricity in energy storage devices to maximize the utilization of electricity. During periods of low electricity prices, the optimization unit prioritizes energy storage charging and discharging, purchasing and storing electrical energy.

[0093] Photovoltaic power generation and energy storage optimization device, including: A photovoltaic array is an arrangement of multiple photovoltaic panels used to convert solar radiation into electrical energy. The photovoltaic array is connected to a power management system via connecting lines. Energy storage device: A device for storing electrical energy generated by photovoltaic power generation. The energy storage device includes multiple battery packs, which are connected to an energy management system via connecting lines. The data acquisition module is used to monitor the power generation of the photovoltaic array in real time and record the light intensity of the area where the photovoltaic array is located; The power management module is used to monitor, control, and optimize the energy flow between photovoltaic power generation and energy storage devices. The power management system includes sensors, control units, data processing units, and optimization units.

[0094] In a preferred embodiment of the present invention, the optimization unit calculates the energy matching relationship between photovoltaic power generation and energy storage based on the following formula: E-matching = α·P photovoltaic - β·P load + γE energy storage; Where Ematching is the energy matching value, α is the weighting coefficient between photovoltaic power generation and energy matching, Pphotovoltaic is the current power of photovoltaic power generation; β is the weighting coefficient between load and energy matching, Pload is the current power of load; γ is the weighting coefficient between energy storage and energy matching; and E is the current energy storage capacity of the energy storage device.

[0095] In a preferred embodiment of the present invention, the optimization unit further includes the following calculation formula: Plight(t) = Ppredict(t) - Ppeak-clipping(t); Pstorage(t) = Pdemand(t) - Plight(t); Wherein, Plight(t) is the power generation at time t, Ppredicted(t) is the predicted power generation, Ppeak shaving(t) is the peak power shaving adjusted according to the peak-valley balance, Pstorage(t) is the energy storage charging and discharging power at time t, and Pdemand(t) is the predicted power demand. P forecast (t) and P demand (t) are calculated based on historical data and power consumption patterns, while P peak shaving (t) is dynamically adjusted based on grid load conditions.

[0096] In a preferred embodiment of the present invention, a database is further included, which is used to store the power generation and light intensity records of the photovoltaic array, and generate a historical power generation change database and a historical light intensity change database, respectively; and obtains a future time t light intensity database through a meteorological center.

[0097] In a preferred embodiment of the present invention, a power prediction module is further included, which is used to extract data on the change of light intensity over time within a future time period t, and to fit and generate a light intensity-time curve q1. Based on the historical light intensity change database, a historical light intensity change curve is generated. The curve segment with the highest similarity to the historical light intensity change curve q1 is extracted from the historical light intensity change curve and named the curve segment as the historical light intensity change curve q2. Therefore, the historical power generation change database corresponding to the time period of the illumination-time curve q2 is extracted, and the power generation data of this time period is marked as P prediction(t).

[0098] In a preferred embodiment of the present invention, the power management system has remote monitoring and control functions, and communicates with a remote server through a communication network to realize remote monitoring, fault diagnosis and parameter adjustment of the system.

[0099] In a preferred embodiment of the present invention, a safety protection module is further included for monitoring and protecting the operational safety of the system, including overvoltage, overcurrent, and overtemperature protection functions.

[0100] In a preferred embodiment of the present invention, the optimization unit obtains real-time electricity price data from the electricity market by interacting with the electricity market. The real-time electricity price data covers the price changes in different time periods, including peak and off-peak periods. Based on the real-time electricity price data, when the electricity price is high, the α weighting coefficient is appropriately increased to enhance the advantages of photovoltaic power generation; when the electricity price is low, the β and γ weighting coefficients are appropriately increased to emphasize the advantages of energy storage charging and discharging. During periods when photovoltaic power generation capacity is sufficient and electricity prices are high, the optimization unit ensures that photovoltaic power generation prioritizes supplying electricity demand and stores excess electricity in energy storage devices to maximize the utilization of electricity. During periods of low electricity prices, the optimization unit prioritizes energy storage charging and discharging, purchasing and storing electrical energy.

[0101] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A photovoltaic power generation and energy storage optimization method, characterized in that, Includes the following steps: A photovoltaic array consisting of multiple photovoltaic panels connected in series and parallel is deployed; the photovoltaic array is connected to a power management system via connecting lines; the photovoltaic array is used to adjust the tilt angle of the panels according to the real-time illumination angle. An energy storage device is configured; the energy storage device includes multiple parallel battery packs and a battery management system; the battery packs are connected to the energy management system via a bidirectional DC / DC converter; wherein the energy storage device is used to regulate power according to charge and discharge commands. The photovoltaic array's power generation and energy storage data are acquired using a light sensor, a power sensor, and an ambient temperature and humidity sensor. The power generation and energy storage data includes: the photovoltaic array's power generation, the light intensity of the area where the photovoltaic array is located, and the ambient temperature and humidity data. Based on the power generation and energy storage data of the photovoltaic array, an optimized photovoltaic power generation and energy storage scheme is determined; the optimized photovoltaic power generation and energy storage scheme includes: the energy matching relationship between photovoltaic power generation and energy storage, the photovoltaic power generation priority supply strategy, and the energy storage charging and discharging power adjustment parameters; According to the photovoltaic power generation and energy storage optimization scheme, the energy flow between photovoltaic power generation and energy storage devices is optimized; the power management system includes sensors, control units, data processing units and optimization units.

2. The photovoltaic power generation and energy storage optimization method according to claim 1, characterized in that, The energy matching relationship between photovoltaic power generation and energy storage is calculated based on the following formula: E-matching = α·P photovoltaic - β·P load + γE energy storage Where Ematching is the energy matching value, α is the weighting coefficient between photovoltaic power generation and energy matching, Pphotovoltaic is the current power of photovoltaic power generation; β is the weighting coefficient between load and energy matching, Pload is the current power of load; γ is the weighting coefficient between energy storage and energy matching; and E is the current energy storage capacity of the energy storage device.

3. The photovoltaic power generation and energy storage optimization method according to claim 2, characterized in that, It also includes the following calculation formulas: Plight(t) = Ppredict(t) - Ppeak clipping(t); Pstorage(t) = Pdemand(t) - Plight(t); Wherein, Plight(t) is the power generation at time t, Ppredicted(t) is the predicted power generation, Ppeak shaving(t) is the peak power shaving adjusted according to the peak-valley balance, Pstorage(t) is the energy storage charging and discharging power at time t, and Pdemand(t) is the predicted power demand. P forecast (t) and P demand (t) are calculated based on historical data and power consumption patterns, while P peak shaving (t) is dynamically adjusted based on grid load conditions.

4. The photovoltaic power generation and energy storage optimization method according to claim 1, characterized in that, A database is constructed to store the power generation and irradiance records of the photovoltaic array, generating historical power generation change database and historical irradiance change database respectively; and a future time t irradiance database is obtained through the meteorological center.

5. The photovoltaic power generation and energy storage optimization method according to claim 1, characterized in that, Extract the data on the change of light intensity over time within a future time period t, and fit and generate a light intensity curve q1 over time; Based on the historical light intensity change database, a historical light intensity change curve is generated. The curve segment with the highest similarity to the historical light intensity change curve q1 is extracted from the historical light intensity change curve and named the curve segment as the historical light intensity change curve q2. Therefore, the historical power generation change database corresponding to the time period of the illumination-time curve q2 is extracted, and the power generation data of this time period is marked as P prediction(t).

6. The photovoltaic power generation and energy storage optimization method according to claim 1, characterized in that, By communicating with a remote server through a communication network, remote monitoring, fault diagnosis, and parameter adjustment of the system can be achieved.

7. The photovoltaic power generation and energy storage optimization method according to claim 1, characterized in that, It also includes a safety protection module for monitoring and protecting the system's operational safety, including overvoltage, overcurrent, and overtemperature protection functions.

8. The photovoltaic power generation and energy storage optimization method according to claim 1, characterized in that, The optimization unit interacts with the electricity market to obtain real-time electricity price data from the electricity market. The real-time electricity price data covers the changes in electricity prices at different times, including peak and off-peak periods. Based on the real-time electricity price data, when the electricity price is high, the α weighting coefficient is appropriately increased to enhance the advantages of photovoltaic power generation; when the electricity price is low, the β and γ weighting coefficients are appropriately increased to emphasize the advantages of energy storage charging and discharging. During periods when photovoltaic power generation capacity is sufficient and electricity prices are high, the optimization unit ensures that photovoltaic power generation prioritizes supplying electricity demand and stores excess electricity in energy storage devices to maximize the utilization of electricity. During periods of low electricity prices, the optimization unit prioritizes energy storage charging and discharging, purchasing and storing electrical energy.

9. A photovoltaic power generation and energy storage optimization system, characterized in that, include: A photovoltaic array is an arrangement of multiple photovoltaic panels used to convert solar radiation into electrical energy, and the photovoltaic array is connected to a power management system via connecting lines. The photovoltaic array is used to adjust the tilt angle of the solar panels according to the real-time illumination angle. Energy storage device: A device for storing electrical energy generated by photovoltaic power generation. The energy storage device includes multiple parallel battery packs and a battery management system. The battery packs are connected to the energy management system through a bidirectional DC / DC converter. The energy storage device is used to regulate power according to charge and discharge commands. The data acquisition module is used to acquire power generation and energy storage data of the photovoltaic array through a light sensor, a power sensor, and an ambient temperature and humidity sensor; the power generation and energy storage data includes: the power generation of the photovoltaic array, the light intensity of the area where the photovoltaic array is located, and the ambient temperature and humidity data; The optimization scheme determination module is used to determine the photovoltaic power generation and energy storage optimization scheme based on the power generation and energy storage data of the photovoltaic array; the photovoltaic power generation and energy storage optimization scheme includes: the energy matching relationship between photovoltaic power generation and energy storage, the photovoltaic power generation priority supply strategy, and the energy storage charging and discharging power adjustment parameters; The power management module is used to optimize the energy flow between photovoltaic power generation and energy storage devices according to the photovoltaic power generation and energy storage optimization scheme. The power management system includes sensors, control units, data processing units, and optimization units.

10. The photovoltaic power generation and energy storage optimization system according to claim 9, characterized in that, The optimization unit calculates the energy matching relationship between photovoltaic power generation and energy storage based on the following formula: E-matching = α·P photovoltaic - β·P load + γE energy storage; Where Ematching is the energy matching value, α is the weighting coefficient between photovoltaic power generation and energy matching, Pphotovoltaic is the current power of photovoltaic power generation; β is the weighting coefficient between load and energy matching, Pload is the current power of load; γ is the weighting coefficient between energy storage and energy matching; and E is the current energy storage capacity of the energy storage device.

11. The photovoltaic power generation and energy storage optimization system according to claim 10, characterized in that, The optimization unit also includes the following calculation formula: Plight(t) = Ppredict(t) - Ppeak clipping(t); Pstorage(t) = Pdemand(t) - Plight(t); Wherein, Plight(t) is the power generation at time t, Ppredicted(t) is the predicted power generation, Ppeak shaving(t) is the peak power shaving adjusted according to the peak-valley balance, Pstorage(t) is the energy storage charging and discharging power at time t, and Pdemand(t) is the predicted power demand. P forecast (t) and P demand (t) are calculated based on historical data and power consumption patterns, while P peak shaving (t) is dynamically adjusted based on grid load conditions.

12. The photovoltaic power generation and energy storage optimization system according to claim 9, characterized in that, It also includes a database for storing records of the photovoltaic array’s power generation and light intensity, generating historical power generation change database and historical light intensity change database respectively; and obtaining a future time t light intensity database through a meteorological center.

13. The photovoltaic power generation and energy storage optimization system according to claim 9, characterized in that, It also includes a power prediction module, which is used to extract data on the change of light intensity over time within a future time period t, and to fit and generate a light intensity-time curve q1. Based on the historical light intensity change database, a historical light intensity change curve is generated. The curve segment with the highest similarity to the historical light intensity change curve q1 is extracted from the historical light intensity change curve and named the curve segment as the historical light intensity change curve q2. Therefore, the historical power generation change database corresponding to the time period of the illumination-time curve q2 is extracted, and the power generation data of this time period is marked as P prediction(t).

14. The photovoltaic power generation and energy storage optimization system according to claim 9, characterized in that, The power management system has remote monitoring and control functions. It communicates with a remote server through a communication network to realize remote monitoring, fault diagnosis and parameter adjustment of the system.

15. The photovoltaic power generation and energy storage optimization system according to claim 9, characterized in that, It also includes a safety protection module for monitoring and protecting the system's operational safety, including overvoltage, overcurrent, and overtemperature protection functions.

16. The photovoltaic power generation and energy storage optimization system according to claim 9, characterized in that, The optimization unit interacts with the electricity market to obtain real-time electricity price data from the electricity market. The real-time electricity price data covers the changes in electricity prices at different times, including peak and off-peak periods. Based on the real-time electricity price data, when the electricity price is high, the α weighting coefficient is appropriately increased to enhance the advantages of photovoltaic power generation; when the electricity price is low, the β and γ weighting coefficients are appropriately increased to emphasize the advantages of energy storage charging and discharging. During periods when photovoltaic power generation capacity is sufficient and electricity prices are high, the optimization unit ensures that photovoltaic power generation prioritizes supplying electricity demand and stores excess electricity in energy storage devices to maximize the utilization of electricity. During periods of low electricity prices, the optimization unit prioritizes energy storage charging and discharging, purchasing and storing electrical energy.

17. A photovoltaic power generation and energy storage optimization device, characterized in that, include: A photovoltaic array is an arrangement of multiple photovoltaic panels used to convert solar radiation into electrical energy, and the photovoltaic array is connected to a power management system via connecting lines. The photovoltaic array is used to adjust the tilt angle of the solar panels according to the real-time illumination angle. Energy storage device: A device for storing electrical energy generated by photovoltaic power generation. The energy storage device includes multiple parallel battery packs and a battery management system. The battery packs are connected to the energy management system through a bidirectional DC / DC converter. The energy storage device is used to regulate power according to charge and discharge commands. The data acquisition module is used to acquire power generation and energy storage data of the photovoltaic array through a light sensor, a power sensor, and an ambient temperature and humidity sensor; the power generation and energy storage data includes: the power generation of the photovoltaic array, the light intensity of the area where the photovoltaic array is located, and the ambient temperature and humidity data; The optimization scheme determination module is used to determine the photovoltaic power generation and energy storage optimization scheme based on the power generation and energy storage data of the photovoltaic array; the photovoltaic power generation and energy storage optimization scheme includes: the energy matching relationship between photovoltaic power generation and energy storage, the photovoltaic power generation priority supply strategy, and the energy storage charging and discharging power adjustment parameters; The power management module is used to optimize the energy flow between photovoltaic power generation and energy storage devices according to the photovoltaic power generation and energy storage optimization scheme. The power management system includes sensors, control units, data processing units, and optimization units.

18. The photovoltaic power generation energy storage optimization device according to claim 17, characterized in that, The optimization unit calculates the energy matching relationship between photovoltaic power generation and energy storage based on the following formula: E-matching = α·P photovoltaic - β·P load + γE energy storage; Where Ematching is the energy matching value, α is the weighting coefficient between photovoltaic power generation and energy matching, Pphotovoltaic is the current power of photovoltaic power generation; β is the weighting coefficient between load and energy matching, Pload is the current power of load; γ is the weighting coefficient between energy storage and energy matching; and E is the current energy storage capacity of the energy storage device.

19. The photovoltaic power generation and energy storage optimization device according to claim 18, characterized in that, The optimization unit also includes the following calculation formula: Plight(t) = Ppredict(t) - Ppeak clipping(t); Pstorage(t) = Pdemand(t) - Plight(t); Wherein, Plight(t) is the power generation at time t, Ppredicted(t) is the predicted power generation, Ppeak shaving(t) is the peak power shaving adjusted according to the peak-valley balance, Pstorage(t) is the energy storage charging and discharging power at time t, and Pdemand(t) is the predicted power demand. P forecast (t) and P demand (t) are calculated based on historical data and power consumption patterns, while P peak shaving (t) is dynamically adjusted based on grid load conditions.

20. The photovoltaic power generation energy storage optimization device according to claim 17, characterized in that, It also includes a database for storing records of the photovoltaic array’s power generation and light intensity, generating historical power generation change database and historical light intensity change database respectively; and obtaining a future time t light intensity database through a meteorological center.

21. The photovoltaic power generation energy storage optimization device according to claim 17, characterized in that, It also includes a power prediction module, which is used to extract data on the change of light intensity over time within a future time period t, and to fit and generate a light intensity-time curve q1. Based on the historical light intensity change database, a historical light intensity change curve is generated. The curve segment with the highest similarity to the historical light intensity change curve q1 is extracted from the historical light intensity change curve and named the curve segment as the historical light intensity change curve q2. Therefore, the historical power generation change database corresponding to the time period of the illumination-time curve q2 is extracted, and the power generation data of this time period is marked as P prediction(t).

22. The photovoltaic power generation energy storage optimization device according to claim 17, characterized in that, The power management system has remote monitoring and control functions. It communicates with a remote server through a communication network to realize remote monitoring, fault diagnosis and parameter adjustment of the system.

23. The photovoltaic power generation and energy storage optimization device according to claim 17, characterized in that, It also includes a safety protection module for monitoring and protecting the system's operational safety, including overvoltage, overcurrent, and overtemperature protection functions.

24. The photovoltaic power generation and energy storage optimization device according to claim 17, characterized in that, The optimization unit interacts with the electricity market to obtain real-time electricity price data from the electricity market. The real-time electricity price data covers the changes in electricity prices at different times, including peak and off-peak periods. Based on the real-time electricity price data, when the electricity price is high, the α weighting coefficient is appropriately increased to enhance the advantages of photovoltaic power generation; when the electricity price is low, the β and γ weighting coefficients are appropriately increased to emphasize the advantages of energy storage charging and discharging. During periods when photovoltaic power generation capacity is sufficient and electricity prices are high, the optimization unit ensures that photovoltaic power generation prioritizes supplying electricity demand and stores excess electricity in energy storage devices to maximize the utilization of electricity. During periods of low electricity prices, the optimization unit prioritizes energy storage charging and discharging, purchasing and storing electrical energy.

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