A solar street lamp charging and discharging management system

Abstract

The present application relates to the technical field of solar power generation, in particular to a kind of solar street lamp's charge-discharge management system, including data acquisition unit, illumination intensity prediction unit, fuzzy inference unit, charging optimization unit, discharge optimization unit and battery management unit.The present application generates first data set by data acquisition unit collection and pre-processes the historical data of street lamp;Illumination intensity prediction unit applies SARIMA model to predict future illumination intensity;Fuzzy inference unit generates membership function according to first data set and predicted illumination intensity;Charging optimization unit constructs fuzzy inference rule base using membership function, calculates current charging power;Discharge optimization unit divides discharge period according to first data set and calculates discharge power;Battery management unit updates battery remaining capacity according to charging power and discharge power, and evaluates energy storage effect, which can improve the energy management effect of solar street lamp, thereby improving the adaptability of solar street lamp's charge-discharge management.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of solar power generation, in particular to a charging and discharging management system of a solar street lamp. BACKGROUND

[0002] A solar street lamp is a lighting device that uses solar energy as the main energy source, widely used in public areas such as streets, squares, gardens and residential areas, etc. It mainly includes a solar panel, a battery, an LED lamp, a controller and related support components. The solar panel is responsible for converting sunlight into electrical energy, which is stored in the battery for use by the LED lamp at night. It not only realizes an environmentally friendly and energy-saving lighting solution, but also reduces dependence on traditional power.

[0003] In a solar street lamp, the charging and discharging management system of the solar street lamp is crucial. The existing charging and discharging management system of the solar street lamp usually relies on a simple controller to manage the charging and discharging process, and automatically turns on and off the lamp according to changes in light intensity. However, this control method has limitations in optimizing the adaptability of charging and discharging management. First of all, it is difficult to respond quickly in conditions of large fluctuations in light conditions, such as sunrise and sunset periods, or short-term cloud cover. The system often cannot quickly adjust to the optimal state, resulting in energy waste or failure to fully utilize available light. Although various schemes have been proposed to use maximum power point tracking (MPPT) technology to improve the charging effect of the solar panel and ensure that it always operates at the maximum power point. However, these schemes often ignore the safety of battery performance, especially in the case of gradually aging batteries, leading to a decline in charging and discharging effect and accelerating system aging. In addition, the existing control strategy also lacks personalized management for residential application scenarios, and fails to optimize according to specific environmental and use requirements, affecting the stability of the system under different environmental conditions and limiting its performance in specific applications. Therefore, the existing charging and discharging management system of the solar street lamp has deficiencies in adaptability, and needs to be further improved to improve its overall performance.

[0004] Therefore, a charging and discharging management system of a solar street lamp is proposed. SUMMARY

[0005] The application aims to provide a solar street lamp charging and discharging management system, which collects and pre-processes historical data of street lamps through a data acquisition unit to generate a first data set; a light intensity prediction unit applies a SARIMA model to predict future light intensity; a fuzzy inference unit generates membership functions according to the first data set and the predicted light intensity value; a charging optimization unit uses the membership functions to build a fuzzy inference rule base and calculates the current charging power; a discharging optimization unit divides the discharging period according to the first data set and calculates the discharging power; and a battery management unit updates the remaining battery capacity according to the charging power and the discharging power and evaluates the energy storage effect, which can improve the energy management effect of the solar street lamp and thus improve the adaptability of the charging and discharging management of the solar street lamp.

[0006] To achieve the above-mentioned purpose, the application provides the following technical scheme.

[0007] A solar street lamp charging and discharging management system comprises:

[0008] A data acquisition unit is configured to acquire historical data of residential solar street lamps and pre-process the historical data of the residential solar street lamps to obtain a first data set.

[0009] A light intensity prediction unit is configured to use a SARIMA model to predict light intensity according to the first data set to obtain a light intensity value.

[0010] A fuzzy inference unit is configured to obtain membership functions of a first fuzzy set, a second fuzzy set and a third fuzzy set according to the first data set and the light intensity value, wherein the membership function of the first fuzzy set is a fuzzy relationship between a voltage adjustment amount and a voltage error, the membership function of the second fuzzy set is a fuzzy relationship between the voltage adjustment amount and a power error, and the membership function of the third fuzzy set is a fuzzy relationship between the voltage adjustment amount and the light intensity value.

[0011] A charging optimization unit is configured to build a fuzzy inference rule base according to the membership functions of the first fuzzy set, the second fuzzy set and the third fuzzy set, obtain a current power according to the first data set and the fuzzy inference rule base, and obtain a charging power according to the current power.

[0012] A discharging optimization unit is configured to divide a discharging period according to the first data set to obtain a discharging power.

[0013] A battery management unit is configured to calculate a remaining battery capacity according to the charging power and the discharging power, and obtain an energy storage effect using an operation constraint condition according to the remaining battery capacity, the charging power and the discharging power.

[0014] Further, the residential solar street lamp historical data comprises:

[0015] The illumination data comprises sunshine intensity and daily illumination duration;

[0016] The battery data comprises charging and discharging historical data, charging efficiency, discharging efficiency, the charging power and the discharging power;

[0017] The weather data comprises temperature, humidity and PM2.5;

[0018] The user data comprises residential user travel time;

[0019] The switch state of the solar street lamp.

[0020] Further, the fuzzy inference unit comprises:

[0021] The voltage error is the difference between the current time voltage and the previous time voltage;

[0022] The power error is the difference between the current time power and the previous time power;

[0023] The current time power is the product of the current time voltage and the current time current;

[0024] The representation of the membership function of the first fuzzy set, the membership function of the second fuzzy set and the membership function of the third fuzzy set is Zadeh representation;

[0025] The membership function of the first fuzzy set, the membership function of the second fuzzy set and the membership function of the third fuzzy set are Cauchy type membership functions.

[0026] Further, the current time power acquisition process comprises:

[0027] According to the first data set, the voltage error, the power error and the sunshine intensity value are fuzzified using Zadeh representation to obtain fuzzy sets;

[0028] According to the fuzzy sets, the membership functions of the first fuzzy set, the membership functions of the second fuzzy set and the membership functions of the third fuzzy set, the membership degrees corresponding to the voltage error, the power error and the sunshine intensity value are calculated respectively;

[0029] According to the membership degrees and the fuzzy inference rule base, inference is carried out to obtain fuzzy output;

[0030] The fuzzy output is de-fuzzled using a barycenter method to obtain a voltage adjustment amount, and the current time power is obtained according to the voltage adjustment amount, a current time voltage and a current time current.

[0031] Further, the fuzzy set comprises:

[0032] The first fuzzy set comprises: a first voltage, a second voltage, a third voltage, a fourth voltage and a fifth voltage;

[0033] The second fuzzy set comprises: a first power, a second power, a third power, a fourth power and a fifth power;

[0034] The third fuzzy set comprises: a first intensity, a second intensity, a third intensity, a fourth intensity and a fifth intensity;

[0035] The fourth fuzzy set comprises: a first increase value, a second increase value, a third increase value, a fourth increase value and a fifth increase value.

[0036] Further, the fuzzy inference rule base comprises:

[0037] The input variables comprise the voltage error, the power error and the illumination intensity value;

[0038] The output variable comprises a voltage adjustment amount;

[0039] The fuzzy states of the input variables are associated with the fuzzy states of the output variable to obtain a membership function of the fourth fuzzy set, and a fuzzy inference rule base is generated according to the membership function of the fourth fuzzy set;

[0040] The rule form of the fuzzy inference rule base is: if the voltage error is X, and the power error is Y, and the illumination intensity value is Z, then the voltage adjustment amount is W; wherein X, Y and Z are the fuzzy states of the input variables, and W is the fuzzy state of the output variable.

[0041] Further, the discharge optimization unit comprises:

[0042] According to the first data set, a discharge time period is divided into an early night time period, a middle night time period, a late night time period and a daytime period;

[0043] If the time period is the early night time period, the discharge power is a load demand power;

[0044] If the time period is the middle night time period, the discharge power is a product of the load demand power and a first discount factor;

[0045] If the time period is the late night time period, the discharging power is a product of the load demand power and a second discount factor;

[0046] If the time period is the daytime time period, the discharging power is zero.

[0047] The discharging power is converted using an S-shaped transition function at the start and end of the early night time period, the middle night time period, the late night time period and the daytime time period.

[0048] Further, if the battery residual capacity is less than a fixed threshold, the discharging power is a product of a previous time discharging power and a third discount factor.

[0049] Further, the battery residual capacity calculation process comprises:

[0050] If the battery is in a charging state, an actual charging capacity is obtained according to a current time charging power and a time interval, and the battery residual capacity is obtained according to a previous time battery residual capacity and the actual charging capacity.

[0051] If the battery is in a discharging state, a discharging capacity is obtained according to a current time discharging power and the time interval, an actual discharging capacity is obtained according to the discharging capacity, and the battery residual capacity is obtained according to the previous time battery residual capacity and the actual discharging capacity.

[0052] Further, the energy storage effect calculation process comprises:

[0053] The operation constraint condition comprises an SOC value constraint condition, a charging power constraint condition and a discharging power constraint condition; wherein the SOC value is a ratio of the battery residual capacity and a maximum capacity of the battery.

[0054] A current time SOC value is obtained according to the operation constraint condition.

[0055] An SOC deviation is calculated according to the current time SOC value and an expected SOC value.

[0056] The SOC deviation is normalized according to the operation constraint condition to obtain the energy storage effect.

[0057] Compared with the prior art, the present application has the following beneficial effects:

[0058] 1、Solar street lights need to achieve efficient charging under different light conditions to maximize the use of solar energy. The system obtains real-time voltage error, power error and light intensity prediction value, and uses these data to build a fuzzy reasoning rule base. The system can dynamically adjust the voltage to maintain the maximum power point, and optimize the charging process by adjusting the charging power, ensuring that the system can achieve the best charging effect under variable light conditions, thereby improving the adaptability and energy utilization efficiency of the system.

[0059] 2、In the operation of solar street lights, the discharge demand at night or under low light conditions often changes. The system dynamically sets reasonable upper and lower limits of discharge power according to power demand at different times to adjust the discharge strategy, so that the battery will not be over-discharged. The system not only responds to changes in power demand in real time, but also ensures that the street light can work continuously and stably at night and on cloudy days, thereby effectively improving the adaptability of the system and the service life of the battery.

[0060] 3、Battery management is crucial to the reliability of the charge and discharge management system of solar street lights. The system calculates the remaining capacity of the battery in real time through the battery management unit, and evaluates the energy storage effect in combination with charging power, discharging power and operating constraints. The system not only optimizes the charging and discharging process of the battery, but also reports the energy storage effect in time for preventive maintenance and adjustment strategy, thereby further improving the overall reliability and adaptability of the charge and discharge management system of solar street lights. BRIEF DESCRIPTION OF DRAWINGS

[0061] Figure 1 A charge and discharge management system flowchart of a solar street light is provided for the embodiments of the present application;

[0062] Figure 2 A charge optimization unit flowchart is provided for the embodiments of the present application;

[0063] Figure 3 An MPTT diagram is provided for the embodiments of the present application;

[0064] Figure 4 A discharge optimization unit flowchart is provided for the embodiments of the present application. DETAILED DESCRIPTION

[0065] The technical solutions in the embodiments of the present application will be described in detail below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor are within the scope of protection of the present application.

[0066] The charge-discharge management system of solar street lamps is widely used in public places such as urban roads, communities, scenic spots, etc. due to its green environmental protection, energy saving and other advantages. The traditional charge-discharge management system of solar street lamps usually includes solar panels, batteries, LED lamps and control systems. Among them, the solar panels are responsible for converting sunlight into electrical energy, the batteries store electrical energy for night use, and the control system is responsible for detecting light intensity, battery charge-discharge management and lamp switching control. This system can reduce dependence on traditional power grids, reduce energy consumption and carbon emissions, and is suitable for lighting needs in unpowered or remote areas.

[0067] However, the existing charge-discharge management system of solar street lamps has some defects that cannot be ignored. First, in unstable light conditions, such as overcast or uneven light, the system is difficult to maintain efficient operation, often leading to energy waste or insufficient battery power. In addition, as the battery ages, the system's ability to monitor and manage the battery's state is weak, further affecting the overall management effect of the system. The existing control system mostly uses a relatively simple charge-discharge control mode, which cannot be flexibly adjusted according to different environmental conditions, and lacks personalized application scenario management, making it difficult to meet the requirements of residential communities for lighting stability and energy saving effect. Therefore, a new system is needed that can more intelligently optimize the charge-discharge effect of solar panels and ensure that the battery always operates in the best working state while improving the system's adaptability in complex environments.

[0068] Please refer to Figures 1 to 4 The present application provides a charge-discharge management system for solar street lamps, taking the solar street lamps of a certain community as the experimental object, aiming to improve the adaptability of the charge-discharge management system of solar street lamps, and the technical solution is as follows:

[0069] As Figure 1 shown, a charge-discharge management system for solar street lamps includes:

[0070] A data acquisition unit is used to acquire residential solar street lamp historical data and preprocess the residential solar street lamp historical data to obtain a first data set.

[0071] Further, the residential solar street lamp historical data includes:

[0072] The light data includes sunshine intensity and daily light duration, and the light data is mainly monitored by a light sensor. The light sensor can monitor the intensity of solar radiation and the duration of light each day in real time. These data can be used to analyze the sunshine conditions under different weather conditions and further optimize the charging effect of solar panels.

[0073] Battery data includes charge and discharge history, charging efficiency, discharging efficiency, charging power and discharging power, etc. The data is obtained by recording the application of street lamps in a certain community or from the manual of solar street lamps;

[0074] Weather data includes temperature, humidity and PM2.5, which records the weather changes according to the data of the meteorological bureau in the area where the community is located, which helps to judge the influence of the environment on the performance of the battery and the photovoltaic panel, and to adjust the charging and discharging strategy;

[0075] User data includes residential user travel time, property usually installs intelligent access control system, monitoring equipment or interacts with the smart home equipment of the residents, and obtains these data through the property, which can help the system to identify the night activities of the residents, and to adjust the on-off time and output power of the street lamp intelligently, to realize more personalized lighting service;

[0076] The on-off state of the solar street lamp, the controller can directly monitor the on-off state of the solar street lamp, record the time of each opening and closing, and further optimize the working mode of the system under different time periods and different lighting conditions.

[0077] Through comprehensive analysis of light, battery state, user behavior and solar street lamp state, the charging and discharging management system of solar street lamp can dynamically adjust the charging and discharging strategy, improve the energy utilization rate, ensure that the battery always runs in the best state, and improve the adaptability of the charging and discharging management system of solar street lamp in complex environment. For example, in a residential area, most of the residents return home after 10 pm, the lighting period of the street lamp can be automatically adjusted according to the user travel mode to reduce energy waste.

[0078] The light intensity prediction unit is configured to predict the light intensity value using a SARIMA model based on the first data set.

[0079] The SARIMA (Seasonal Autoregressive Integrated Moving Average) model is a seasonal difference autoregressive moving average model, which can capture the trend and seasonal changes in the data.

[0080] Further, according to the first data set, the weather data and light data of a certain community from 5 am to 6 pm are obtained, as shown in Table 1, which reflects the light intensity value under different temperature and humidity conditions;

[0081] Table 1. Weather data and light data

[0082] date time Illumination intensity (W / m 2 )]]> Temperature (℃) humidity(%) 2024-04-01 05:00:00 300 16 60 2024-04-01 05:30:00 312 16 58 2024-04-01 06:00:00 341 17 60 2024-04-01 06:30:00 356 18 62

[0083] Taking the light intensity value as a target variable, temperature and humidity as external regression variables of the model, and splitting the first data set into a training set and a test set;

[0084] The training set is input into the SARIMA model, the SARIMA model differentiates the external input variables to eliminate the trend fluctuations in the data, combines the autoregressive term and the moving average term, and fits the change pattern of the light intensity;

[0085] After the SARIMA model is trained, the SARIMA model is used to predict the light intensity in the next day. The test set is input into the SARIMA model, and the SARIMA model can output accurate light intensity values, thereby providing data support for the subsequent charging optimization unit.

[0086] The fuzzy inference unit is configured to obtain a membership function of a first fuzzy set, a membership function of a second fuzzy set, and a membership function of a third fuzzy set according to the first data set and the light intensity value, the membership function of the first fuzzy set being a fuzzy relationship between a voltage adjustment amount and a voltage error, the membership function of the second fuzzy set being a fuzzy relationship between the voltage adjustment amount and a power error, and the membership function of the third fuzzy set being a fuzzy relationship between the voltage adjustment amount and the light intensity value.

[0087] Further, the fuzzy inference unit includes:

[0088] The voltage error is a difference between a current time voltage and a previous time voltage, and is represented as:

[0089] ΔV = V(t) - V(t-1);

[0090] wherein ΔV is the voltage error, V(t) is the voltage at time t, and V(t-1) is the voltage at time t-1;

[0091] The power error is a difference between the current time power and the previous time power, and is represented as:

[0092] ΔP = P(t) - P(t-1);

[0093] wherein ΔP is the power error, P(t) is the power at time t, and P(t-1) is the power at time t-1;

[0094] The current time power is a product of the current time voltage and the current time current, and is represented as:

[0095] P(t) = V(t) x I(t);

[0096] wherein I(t) is the current at time t;

[0097] The voltage adjustment amount is used to adjust the duty cycle of the DC-DC converter, so as to change the working voltage of the battery panel, which is expressed as:

[0098] ΔD=f(ΔP,ΔV,S);

[0099] Wherein, ΔD is the voltage adjustment amount, f is the fuzzy inference rule base, and S is the light intensity value;

[0100] The membership functions of the first fuzzy set, the second fuzzy set and the third fuzzy set are all expressed in Zadeh representation;

[0101] Wherein, the Zadeh representation is a membership representation method, in which the membership of each element in the fuzzy set varies between 0 and 1, and is used to represent the membership degree of the element to the set. The membership of 1 indicates that the element completely belongs to the set, the membership of 0 indicates that it does not belong at all, and the value between 0 and 1 indicates partial membership;

[0102] Specifically, in the first fuzzy set, the concept of voltage error "fifth voltage" is represented by Zadeh representation. Assuming that the membership of voltage error 2 is 0.2, the membership of 4 is 0.6, and the membership of 6 is 0.9, it means that the membership of voltage error 6 to the concept of "fifth voltage" is the largest, and the membership is lower when the voltage error is 2.

[0103] The membership functions of the first fuzzy set, the second fuzzy set and the third fuzzy set are all Gaussian membership functions;

[0104] Wherein, the Gaussian membership function is a membership function with smooth transition characteristics, which represents the membership in the fuzzy set by Gaussian distribution, and is expressed as:

[0105]

[0106] Wherein, f(x) is the membership value, x is the input variable; c is the center value, and a is the parameter for controlling the membership change speed.

[0107] Specifically, assuming that the voltage of the solar street lamp at the current moment is 9V, and the voltage at the previous moment is 12V, the voltage error is -3V, the membership in the "first voltage" is 0.059, the membership in the "second voltage" is 0.2, the membership in the "third voltage" is 0.027, the membership in the "fourth voltage" is 0.0099, and the membership in the "fifth voltage" is 0.0039. After comparison, the system can consider that the voltage error is "second voltage". Through the fuzzy inference unit, the charge and discharge management system of the solar street lamp can comprehensively process multiple input variables such as voltage error, power error and light intensity value, and based on these variables, the voltage adjustment amount of the solar street lamp can be dynamically adjusted, thereby enhancing the adaptability of the charge and discharge management system of the solar street lamp to environmental changes. In addition, the use of Cauchy membership function can ensure smooth transition in the membership calculation process, reduce the sharp fluctuations of the charge and discharge management system of the solar street lamp in response to changes in light intensity, and thus improve the stability and adaptability of the charge and discharge management system control of the solar street lamp.

[0108] The charging optimization unit, as shown in Figure 2 constructs a fuzzy inference rule base according to the membership functions of the first fuzzy set, the membership functions of the second fuzzy set and the membership functions of the third fuzzy set, obtains the current moment power according to the first data set and the fuzzy inference rule base, and obtains the charging power according to the current moment power.

[0109] The current moment power is the maximum power point (MPP), as shown in Figure 3 Through maximum power point tracking (MPPT), the system can adjust the working state of the battery panel in real time to ensure that the maximum power can be extracted under any environmental conditions.

[0110] Further, the fuzzy set includes:

[0111] The first fuzzy set includes: "first voltage (VL)", "second voltage (LL)", "third voltage (NM)", "fourth voltage (HH)" and "fifth voltage (VH)";

[0112] The first voltage represents the case of excessively low voltage, the second voltage represents the case of low voltage, the third voltage represents the normal voltage, the fourth voltage represents the case of high voltage, and the fifth voltage represents the case of excessively high voltage.

[0113] The second fuzzy set includes: "first power (VL)", "second power (LL)", "third power (NM)", "fourth power (HH)" and "fifth power (VH)".

[0114] wherein the first power represents a case of power being too low, the second power represents a case of power being low, the third power represents a case of power being normal, the fourth power represents a case of power being high, and the fifth power represents a case of power being too high;

[0115] The third fuzzy set includes: “first intensity (H)”, “second intensity (HW)”, “third intensity (M)”, “fourth intensity (LW)”, and “fifth intensity (W)”

[0116] wherein the first intensity represents a case of light intensity being very strong, the second intensity represents a case of light intensity being strong, the third intensity represents a case of light intensity being moderate, the fourth intensity represents a case of light intensity being weak, and the fifth intensity represents a case of light intensity being very weak;

[0117] The fourth fuzzy set includes: “first increase value (IM)”, “second increase value (IL)”, “third increase value (NM)”, “fourth increase value (DL)”, and “fifth increase value (DM)”

[0118] wherein the first increase value represents a case of voltage adjustment amount increasing a lot, the second increase value represents a case of voltage adjustment amount increasing a little, the third increase value represents a case of voltage adjustment amount being unchanged, the fourth increase value represents a case of voltage adjustment amount decreasing a little, and the fifth increase value represents a case of voltage adjustment amount decreasing a lot;

[0119] Specifically, the first fuzzy set is used to describe the voltage error, the second fuzzy set is used to describe the power error, the third fuzzy set is used to describe the light intensity value, and the fourth fuzzy set is used to describe the voltage adjustment amount. The control mode using specific numerical values can be too rigid in some cases and difficult to adapt to changing environmental conditions, while the use of these fuzzy sets helps the system to make flexible adjustments according to the actual situation. For example, a voltage error of -4V is not an absolute low value, but in a specific situation, such as weak light intensity, it can be classified as “first voltage”, so that appropriate adjustment strategies are triggered according to the fuzzy inference rule base. Through this hierarchical approach, the system can intelligently classify and make decisions on complex environmental signals without relying on absolute numerical values, thereby improving the adaptability of the solar street lamp in charge and discharge management.

[0120] Further, the fuzzy inference rule base includes:

[0121] The input variables include the voltage error, the power error, and the light intensity value;

[0122] The output variable includes the voltage adjustment amount;

[0123] The fuzzy state of the input variable is associated with the fuzzy state of the output variable to obtain a membership function of a fourth fuzzy set, and a fuzzy reasoning rule base is generated according to the membership function of the fourth fuzzy set;

[0124] The rule form of the fuzzy reasoning rule base is: if the voltage error is X, and the power error is Y, and the illumination intensity is Z, then the voltage adjustment amount is W; wherein X, Y, Z are the fuzzy state of the input variable, and W is the fuzzy state of the output variable.

[0125] Table 2. Partial fuzzy reasoning rule base

[0126] Rule Number Voltage error (X) Power error (Y) Light intensity (Z) Voltage adjustment (W) 1 VL VL H IM 2 VL VL HW IL 3 VL VL M NM 4 VL VL LW DL

[0127] Specifically, the complete fuzzy reasoning rule base includes all possible combinations of input variables, a total of 125 rules, as shown in Table 2, which shows how different combinations of input variables are mapped to the corresponding voltage adjustment amount, expressed in words as:

[0128] The first rule is: if the voltage error is the first voltage, and the power error is the first power, and the illumination intensity is the first intensity, then the voltage adjustment amount is the first increase value;

[0129] The second rule is: if the voltage error is the first voltage, and the power error is the first power, and the illumination intensity is the second intensity, then the voltage adjustment amount is the second increase value;

[0130] The third rule is: if the voltage error is the first voltage, and the power error is the first power, and the illumination intensity is the third intensity, then the voltage adjustment amount is the third increase value;

[0131] The fourth rule is: if the voltage error is the first voltage, and the power error is the first power, and the illumination intensity is the fourth intensity, then the voltage adjustment amount is the fourth increase value.

[0132] In the charge-discharge management system of the solar street lamp, through the combination of the reasoning rule base and the maximum power point tracking technology, it can ensure that the solar panel can always extract the maximum power under various illumination conditions, whether it is sunny or cloudy, the system can adjust the working state of the panel in real time to maximize the conversion effect of solar energy, thereby improving energy utilization and improving the adaptability of the charge-discharge management of the solar street lamp.

[0133] Further, the current time power acquisition process includes:

[0134] According to the first data set, the voltage error, the power error and the illumination intensity value are fuzzified using Zadeh representation method to obtain a fuzzy set;

[0135] According to the fuzzy set, the membership functions of the first fuzzy set, the membership functions of the second fuzzy set and the membership functions of the third fuzzy set, the membership degrees corresponding to the voltage error, the power error and the illumination intensity value are respectively calculated using Cauchy type membership functions;

[0136] Table 3. Membership function interval of the first fuzzy set

[0137] Fuzzy state VL LL NM HH VH Center value c -6 -3 0 3 6 Control parameter a 0.5 0.5 0.5 0.5 0.5

[0138] Table 4. Membership function interval of the second fuzzy set

[0139] Fuzzy state VL LL NM HH VH Center value c -5 -2 0 2 5 Control parameter a 0.5 0.5 0.5 0.5 0.5

[0140] Table 5. Membership function interval of the third fuzzy set

[0141] Fuzzy state VL LL NM HH VH Center value c 100 300 500 800 1000 Control parameter a 100 100 100 100 100

[0142] Wherein, according to the first data set, the power of the solar street lamp in a certain cell is 24W, the voltage is 12V, and the average daytime illumination intensity is about 600W / m 2 Therefore, the membership function interval of the fuzzy set is shown in Tables 3 to 5, and each column represents the center value c of a fuzzy state and the fuzzy range control parameter a.

[0143] According to the membership and the fuzzy inference rule base, inference is performed to obtain a fuzzy output;

[0144] The fuzzy output is de-fuzzified using the barycenter method to obtain a voltage adjustment amount, and the current time power is obtained according to the voltage adjustment amount, the current time voltage and the current time current.

[0145] Wherein, the barycenter method is one of the commonly used de-fuzzification methods in fuzzy control system, which is used to convert fuzzy output into accurate value, and is expressed as:

[0146]

[0147] Wherein, z * is the accurate output value after de-fuzzification; z is the value of the output variable, which is a continuous value; u(z) is the value of the membership function corresponding to the value z; the numerator is the integral with weight, which represents the weighted average of all possible output values z; the denominator is the total area of the membership function, which ensures that the output is normalized;

[0148] Specifically, assume that the voltage error of the solar street light at a certain moment is -2V, the power error is -5W, and the light intensity is 800W / m 2 First, the Zadeh representation is used to fuzzify these data, and the membership of -2V in the "first voltage" set is calculated to be 0.8, and the membership of -5W in the "first power" set is 0.7, 800W / m 2 The degree of membership in the "first intensity" set is 0.9. Based on the fuzzy inference rule base, the voltage adjustment is calculated as the "first increase value." This fuzzy output, "first increase value," is defuzzified using the center of gravity method, resulting in a voltage adjustment of 3V. Assuming the current voltage is 10V and the current is 1.5A, the adjusted power is 19.5W. The charging optimization unit effectively handles voltage and power errors and light intensity, precisely adjusting voltage to optimize power output. This allows the system to provide stable lighting under varying environmental conditions, thereby improving the adaptability of the solar street light's charge and discharge management.

[0149] Further, if Figure 4 As shown, the discharge optimization unit includes:

[0150] Dividing the discharge time period into an early night time period, a mid night time period, a late night time period, and a day time period according to the first data set;

[0151] If the time period is the early night time period, the discharge power is the load demand power, which is generally the default power of the solar street light, expressed as:

[0152] p out (t) = p need ,t∈[t1,t2];

[0153] Among them, p out (t) is the discharge power at time t, p need is the load demand power, t1 is the upper limit of the early night time, and t2 is the lower limit of the early night time;

[0154] If the time period is the mid-night time period, the discharge power is the product of the load demand power and the first discount factor, which is expressed as:

[0155] p out (t) = p need ×w1,t∈[t2,t3];

[0156] Among them, t3 is the lower limit of the middle time of night; w1 is the first discount factor, which is used to reduce brightness and save power, and is set to 0.8;

[0157] If the time period is the late night time period, the discharging power is the product of the load demand power and a second discount factor, denoted as:

[0158] p out (t) = p need × w2, t e [t3, t4] ;

[0159] where t4 is the lower limit of the late night time; w2 is the second discount factor, which is set to 0.6 for further saving of electricity; if the time period is the daytime period, the discharging power is zero, denoted as:

[0160] p out (t) = 0, t e [t4, ti] ;

[0161] The discharging power is converted using a sigmoid transition function at the beginning and end of the early night time period, the middle night time period, the late night time period, and the daytime period.

[0162] where the sigmoid transition function can make the transition of the time period smoother, denoted as:

[0163]

[0164] where p out (t+1) is the discharging power at t+1; p out (t-1) is the discharging power at t-1; t0 is the transition end point; c is a parameter for adjusting the transition speed, which is set to 0.5.

[0165] Specifically, the discharging time period is divided according to user data, weather data, and light intensity, as shown in Table 6, which shows an example of discharging time period division in April in a certain community. It is worth noting that the start and end points of the discharging time period are not fixed, but are determined by weather data and light intensity values. If the daytime visibility is low, i.e., the humidity is greater than 90% or PM2.5 is greater than 150 μg / m 3 or the light intensity is less than 500 W / m 2 , discharging still occurs.

[0166] Table 6. Discharging time period division

[0167] Time period Daytime Early night time Mid-night time period Late night time Specific time 05:00-18:00 18:00-21:00 21:00-00:00 00:00-05:00 <![CDATA[t0]]> 06:00 19:00 22:00 01:00

[0168] The discharge optimization unit sets the discount factor at different time periods to avoid long-time high-power continuous discharge and reduce the load of the battery. Meanwhile, the introduction of the S-shaped transition function makes the adjustment of the discharge power more smooth, avoiding the impact of sudden voltage fluctuations on the battery and lighting equipment. In addition, when the light intensity is insufficient and the weather is bad (such as foggy and rainy days), the system can automatically determine and appropriately extend the discharge time at night to ensure sufficient lighting in low visibility conditions, increasing the safety of community roads, especially the protection of pedestrians at night, thereby improving the adaptability of the solar street lamp's charge and discharge management.

[0169] Further, if the remaining battery power is less than a fixed threshold, the discharge power is the product of the last time discharge power and a third discount factor, denoted as:

[0170] p out (t)=p out (t-1)×w3,if E bat (t)≤E min ;

[0171] where w3 is the third discount factor, set to 0.8; E bat (t) is the remaining battery power at time t; E min is a fixed threshold, set to a SOC value equal to 30%, and the SOC value is the state of charge of the battery.

[0172] Specifically, assuming that the initial battery power of a solar street lamp is 50%. In the early night period, the system discharges according to the set load demand power. As time goes on, in the late night period, the remaining battery power gradually decreases, and when the power drops to a SOC value of 30%, the system automatically starts the protection mechanism to adjust the discharge power in the late night period using the third discount factor. This adjustment not only effectively extends the discharge time, especially in the case of insufficient light such as overcast and rainy days, preventing lighting interruption caused by power depletion, but also avoids damage to the battery caused by excessive discharge, thereby improving the adaptability of the solar street lamp's charge and discharge management and the battery protection capability.

[0173] Further, the remaining battery power calculation process includes:

[0174] Set the initial battery power to 0;

[0175] If the battery is in a charging state, the actual charging capacity is obtained according to the current charging power, charging efficiency and time interval, and the remaining battery power is obtained according to the remaining battery power at the last time and the actual charging capacity, denoted as:

[0176] E bat (t)=Ebat (t-1) + p charge (t) x Δt x w charge ;

[0177] where E bat (t) is the remaining battery power at time t; p charge (t) is the charging power at time t; Δt is the time interval, which is 30 minutes; w charge is the charging efficiency; E bat (t-1) is the remaining battery power at time t-1;

[0178] If the battery is in a discharging state, the discharge power at the current time and the time interval are used to obtain the discharge amount, the actual discharge amount is obtained according to the discharge amount and the discharge efficiency, and the remaining battery power is obtained according to the remaining battery power at the last time and the actual discharge amount, which is represented as:

[0179]

[0180] where p discharge (t) is the discharge power at time t, and w discharge is the discharge efficiency;

[0181] where the charging efficiency w charge and the discharge efficiency w discharge are calculated from the first data set or obtained according to the solar street lamp safety manual;

[0182] Specifically, by accurately calculating the change of electric quantity in the charging and discharging process, the charging and discharging management system of the solar street lamp can optimize the use of the battery and improve energy utilization. The system adjusts the strategy in real time according to the actual charging and discharging conditions to maximize the use of solar energy. For example, in cloudy weather, the solar panel receives less light, and the system can automatically adjust the charging power to ensure charging under the best possible conditions. In addition, the system can effectively prevent the battery from running out of power, so as to avoid the solar street lamp from not working normally at night or on cloudy days, and improve the reliability and adaptability of the system. For example, if the night power is close to the minimum limit, the system will automatically reduce the discharge power to ensure that the light will not be extinguished due to insufficient power.

[0183] Further, the energy storage effect calculation process includes:

[0184] The operation constraint conditions include SOC value constraint conditions, charging power constraint conditions and discharging power constraint conditions;

[0185] where the SOC value constraint condition is represented as:

[0186] S min ≤ SOC(t) ≤ Smax ;

[0187] wherein S min is the minimum value of the SOC value, being 10%; S max is the maximum value of the SOC value, being 100%; SOC(t) is the SOC value at time t, the SOC value being the ratio of the remaining capacity of the battery to the maximum capacity of the battery;

[0188] wherein the SOC value at time t is expressed as:

[0189]

[0190] wherein E max is the maximum storage capacity of the battery, being 100 AH; SOC(t) is the state of charge of the battery at time t;

[0191] The charging power constraint condition is expressed as:

[0192] p charge,min ≤ p charge (t) ≤ p charge,max ;

[0193] wherein p charge,min is the minimum value of the charging power, being 0 W; p charge,max is the maximum value of the charging power, being 24 W; p charge (t) is the charging power at time t;

[0194] The discharging power constraint condition is expressed as:

[0195] p discharge,min ≤ p discharge (t) ≤ p discharge,max ;

[0196] wherein p discharge,min is the minimum value of the discharging power, being 4.8 W; p discharge,max is the maximum value of the discharging power, being 24 W; p discharge (t) is the discharging power at time t;

[0197] According to the running constraint condition, the SOC value at the current time is obtained, according to the SOC value at the current time and the expected SOC value, the SOC deviation is calculated, and the SOC deviation is normalized according to the running constraint condition, to obtain the energy storage effect, which is expressed by the formula:

[0198]

[0199] wherein C soc is the energy storage effect; S targetFor the SOC expected value, it is set to 80% during daytime charging and 20% during nighttime use; in addition, by multiplying the difference by 2, the range of the balance index can be adjusted to the interval [-1, 1], which can intuitively reflect the deviation degree of the actual SOC value from the expected SOC value.

[0200] Specifically, in the charge-discharge management system of the solar street lamp, the energy storage effect refers to maintaining the battery's power within a reasonable range through optimized charge-discharge management, thereby achieving efficient use of energy and prolonging battery life. Through real-time calculation of the energy storage effect, the system can timely report the energy storage effect in order to carry out preventive maintenance and adjustment strategies, for example, when the daytime SOC value is lower than the SOC expected value, the system will increase the charging power to increase the battery power; conversely, when the daytime SOC value is higher than the SOC expected value, the system will reduce the charging power or increase the discharging power to avoid overcharging the battery, not only reducing battery damage caused by over-discharge or overcharge, but also improving the overall reliability and adaptability of the system.

[0201] In summary, by using the SARIMA model to predict the light intensity with the light intensity prediction unit, the system can obtain the future light conditions in advance, and according to the prediction results, the fuzzy reasoning unit and the charging optimization unit, the system can optimize the voltage adjustment and charging strategy, thereby achieving the best charging effect under various light conditions. Through the discharge optimization unit, the discharge period is intelligently divided according to historical data, and the discharge power is dynamically adjusted according to actual needs. This not only effectively avoids over-discharge of the battery, but also ensures stable operation of the street lamp at night or in low light conditions. Through the battery management unit, the remaining battery power is dynamically calculated, and combined with the charging and discharging power, it ensures that the battery will not overcharge or over-discharge during charging and discharging, effectively prolonging the battery life and improving the energy storage effect, thereby improving the charging and discharging management adaptability of the solar street lamp.

[0202] Although embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and alterations can be made thereto without departing from the principles and spirit of the present application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A solar street light charge and discharge management system, characterized in that: include: A data acquisition unit is used to acquire historical data of residential solar street lights and preprocess the historical data of residential solar street lights to obtain a first data set; a light intensity prediction unit is used to predict light intensity using a SARIMA model based on the first data set to obtain a light intensity value; a fuzzy inference unit, configured to obtain, based on the first data set and the light intensity value, a membership function of a first fuzzy set, a membership function of a second fuzzy set, and a membership function of a third fuzzy set, wherein the membership function of the first fuzzy set is a fuzzy relationship between a voltage adjustment amount and a voltage error, the membership function of the second fuzzy set is a fuzzy relationship between the voltage adjustment amount and a power error, and the membership function of the third fuzzy set is a fuzzy relationship between the voltage adjustment amount and the light intensity value; a charging optimization unit, configured to construct a fuzzy inference rule base based on the membership function of the first fuzzy set, the membership function of the second fuzzy set, and the membership function of the third fuzzy set, and input variables including the voltage error, the power error, and the light intensity value, obtain the current power based on the first data set and the fuzzy inference rule base, and obtain the charging power based on the current power; a discharge optimization unit, configured to divide the discharge time period according to the first data set to obtain a discharge power; The battery management unit is used to calculate the remaining battery capacity according to the charging power and the discharging power, and obtain the energy storage effect according to the remaining battery capacity using the operation constraint conditions.

2. A solar street light charge and discharge management system according to claim 1, characterized in that: The historical data of the residential solar street lights include: lighting data including sunshine intensity and daily lighting duration; battery data including charging and discharging history data, charging efficiency, discharge efficiency, the charging power and the discharge power; weather data including temperature, humidity and PM2.5; user data including residential user travel time; and the on / off status of the solar street lights.

3. A solar street light charge and discharge management system according to claim 1, characterized in that: The fuzzy reasoning unit includes: the voltage error is the difference between the voltage at the current moment and the voltage at the previous moment; the power error is the difference between the power at the current moment and the power at the previous moment; the power at the current moment is the product of the voltage at the current moment and the current at the current moment; the representation of the membership function of the first fuzzy set, the membership function of the second fuzzy set, and the membership function of the third fuzzy set are all Zadeh representation; the membership function of the first fuzzy set, the membership function of the second fuzzy set, and the membership function of the third fuzzy set are all Cauchy-type membership functions.

4. A solar street light charge and discharge management system according to claim 1, characterized in that: The power acquisition process at the current moment includes: according to the first data set, using Zadeh representation to fuzzify the voltage error, the power error and the light intensity value to obtain a fuzzy set; according to the fuzzy set, the membership function of the first fuzzy set, the membership function of the second fuzzy set and the membership function of the third fuzzy set, respectively calculating the membership corresponding to the voltage error, the power error and the light intensity value; reasoning based on the membership and the fuzzy inference rule base to obtain a fuzzy output; defuzzifying the fuzzy output using the center of gravity method to obtain a voltage adjustment amount, and obtaining the power at the current moment based on the voltage adjustment amount, the voltage at the current moment and the current at the current moment.

5. A solar street light charge and discharge management system according to claim 4, characterized in that: The fuzzy set includes: the first fuzzy set includes: first voltage, second voltage, third voltage, fourth voltage and fifth voltage; the second fuzzy set includes: first power, second power, third power, fourth power and fifth power; the third fuzzy set includes: first intensity value, second intensity, third intensity, fourth intensity and fifth intensity; the fourth fuzzy set includes: first increase value, second increase value, third increase value, fourth increase value and fifth increase value.

6. A solar street light charge and discharge management system according to claim 1, characterized in that: The fuzzy inference rule base includes: input variables include the voltage error, the power error and the light intensity value; the output variable includes the voltage adjustment amount; the fuzzy state of the input variable is associated with the fuzzy state of the output variable to obtain the membership function of the fourth fuzzy set, and the fuzzy inference rule base is generated according to the membership function of the fourth fuzzy set; the rule form of the fuzzy inference rule base is: if the voltage error is X, the power error is Y, and the light intensity value is Z, then the voltage adjustment amount is W; wherein X, Y, and Z are the fuzzy states of the input variables, and W is the fuzzy state of the output variable.

7. A solar street light charge and discharge management system according to claim 1, characterized in that: The discharge optimization unit includes: dividing the discharge time period into an early night time period, a mid-night time period, a late night time period and a daytime time period according to the first data set; if the time period is the early night time period, the discharge power is the load demand power; if the time period is the mid-night time period, the discharge power is the product of the load demand power and a first discount factor; if the time period is the late night time period, the discharge power is the product of the load demand power and a second discount factor; if the time period is the daytime time period, the discharge power is zero; and using an S-type transition function to transform the discharge power at the beginning and end of the early night time period, the mid-night time period, the late night time period and the daytime time period.

8. A solar street light charge and discharge management system according to claim 7, characterized in that: If the remaining battery power is less than a fixed threshold, the discharge power is the product of the discharge power at the previous moment and a third discount factor.

9. A solar street light charge and discharge management system according to claim 1, characterized in that: The battery remaining capacity calculation process includes: if the battery is in a charging state, obtaining the actual charge capacity according to the current charging power and the time interval, and obtaining the battery remaining capacity according to the battery remaining capacity at the previous moment and the actual charge capacity; if the battery is in a discharging state, obtaining the discharge capacity according to the current discharge power and the time interval, obtaining the actual discharge capacity according to the discharge capacity, and obtaining the battery remaining capacity according to the battery remaining capacity at the previous moment and the actual discharge capacity.

10. A solar street light charge and discharge management system according to claim 1, characterized in that: The energy storage effect calculation process includes: the operating constraints include an SOC value constraint, a charging power constraint, and a discharging power constraint; wherein the SOC value is a ratio of the remaining battery power to the maximum battery capacity; obtaining a current SOC value based on the operating constraints; and calculating an SOC deviation based on the current SOC value and a desired SOC value; The SOC deviation is normalized according to the operating constraint condition to obtain the energy storage effect.

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