Charging and discharging management system of solar street lamp

Abstract

The invention relates to the technical field of solar power generation, in particular to a charging and discharging management system of a solar street lamp, which comprises a data acquisition unit, an illumination intensity prediction unit, a fuzzy reasoning unit, a charging optimization unit, a discharging optimization unit and a battery management unit. Historical data of street lamps are collected and preprocessed through a data acquisition unit, and a first data set is generated; the illumination intensity prediction unit applies an SARIMA model to predict future illumination intensity; the fuzzy reasoning unit generates a membership function according to the first data set and the predicted illumination intensity; the charging optimization unit constructs a fuzzy inference rule base by using a membership function and calculates the current charging power; the discharge optimization unit divides discharge time periods according to the first data set and calculates discharge power; the battery management unit updates the remaining capacity of the storage battery according to the charging power and the discharging power and evaluates the energy storage effect, the energy management effect of the solar street lamp can be improved, and therefore the adaptability of charging and discharging management of the solar street lamp is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of solar power generation, and in particular to a charge and discharge management system for a solar street lamp. Background Art

[0002] Solar street lights are lighting equipment that use solar energy as the main energy source. They are widely used in public areas such as streets, squares, gardens and residential areas. They mainly include solar panels, batteries, LED lamps, controllers and related bracket components. Solar panels are responsible for converting sunlight into electrical energy, which is stored in batteries for use by LED lamps at night. This not only realizes an environmentally friendly and energy-saving lighting solution, but also reduces dependence on traditional electricity.

[0003] In solar street lights, the charge and discharge management system of solar street lights is crucial. The existing charge and discharge management system of solar street lights usually relies on simple controllers to manage the charging and discharging process, and automatically turns on and off the lamps according to the changes in light intensity. However, this control method has limitations in optimizing the adaptability of charge and discharge management. First, it is difficult to respond quickly when the light conditions fluctuate greatly. For example, during sunrise and sunset, or when there is a short period of cloud cover, the system often cannot quickly adjust to the optimal state, resulting in energy waste or failure to fully utilize the available light. Although a variety of solutions using maximum power point tracking (MPPT) technology have been proposed, which can improve the charging effect of solar panels and ensure that they always operate at the maximum power point. However, these solutions often ignore the safety issues of battery performance, especially when the battery is gradually aging, resulting in a decrease in the charge and discharge effect and accelerated system aging. In addition, the existing control strategy also lacks personalized management for residential application scenarios, and fails to be optimized according to specific environments and usage requirements, which not only affects the stability of the system under different environmental conditions, but also limits its performance in specific applications. Therefore, the existing charge and discharge management system of solar street lights has deficiencies in adaptability and needs to be further improved to improve its overall performance.

[0004] Therefore, a charging and discharging management system for a solar street lamp is proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a charge and discharge management system for solar street lamps. The data acquisition unit collects and preprocesses the historical data of the street lamps to generate a first data set; the light intensity prediction unit uses the SARIMA model to predict the future light intensity; the fuzzy reasoning unit generates a membership function according to the first data set and the predicted light intensity value; the charging optimization unit uses the membership function to build a fuzzy reasoning rule base to calculate the current charging power; the discharge optimization unit divides the discharge time period according to the first data set and calculates the discharge power; the battery management unit updates the remaining battery power 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, thereby improving the adaptability of the charge and discharge management of the solar street lamp.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A charging and discharging management system for a solar street lamp, comprising:

[0008] A data acquisition unit, used to acquire historical data of residential solar street lights, and pre-process the historical data of residential solar street lights to obtain a first data set;

[0009] A light intensity prediction unit, configured to predict the light intensity using a SARIMA model according to the first data set to obtain a light intensity value;

[0010] a fuzzy reasoning unit, configured to obtain, according to 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;

[0011] a charging optimization unit, configured to construct a fuzzy inference rule base according to 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, obtain the current moment power according to the first data set and the fuzzy inference rule base, and obtain the charging power according to the current moment power;

[0012] a discharge optimization unit, configured to divide the discharge time period according to the first data set to obtain the discharge power;

[0013] The battery management unit is used to calculate the remaining power of the battery according to the charging power and the discharging power, and obtain the energy storage effect according to the remaining power of the battery, the charging power and the discharging power using the operation constraint conditions.

[0014] Furthermore, the residential solar street light historical data includes:

[0015] Light data includes sunlight intensity and daily sunlight duration;

[0016] The battery data includes charge and discharge history data, charging efficiency, discharging efficiency, the charging power and the discharging power;

[0017] Weather data includes temperature, humidity and PM2.5;

[0018] User data include travel times for residential users;

[0019] The on / off state of the solar street light.

[0020] Furthermore, the fuzzy reasoning unit comprises:

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

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

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

[0024] The representation methods 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;

[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 all Cauchy-type membership functions.

[0026] Furthermore, the power acquisition process at the current moment includes:

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

[0028] 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, the membership corresponding to the voltage error, the power error and the light intensity value are calculated respectively;

[0029] Perform reasoning based on the membership degree and the fuzzy reasoning rule base to obtain a fuzzy output;

[0030] The fuzzy output is defuzzified using a centroid method to obtain a voltage adjustment amount, and the current moment power is obtained according to the voltage adjustment amount, the current moment voltage, and the current moment current.

[0031] Furthermore, the fuzzy set includes:

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

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

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

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

[0036] Furthermore, the fuzzy reasoning rule base includes:

[0037] The input variables include the voltage error, the power error and the light intensity value;

[0038] Output variables include voltage adjustment;

[0039] Associating the fuzzy state of the input variable with the fuzzy state of the output variable to obtain a membership function of a fourth fuzzy set, and generating a fuzzy inference rule base 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, 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.

[0041] Furthermore, the discharge optimization unit comprises:

[0042] 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;

[0043] If the time period is the nighttime initial time period, the discharge power is the load demand power;

[0044] 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;

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

[0046] If the time period is the daytime time period, the discharge power is zero;

[0047] The discharge power is shifted using an S-shaped transition function at the beginning and end of the early night time period, the mid night time period, the late night time period, and the day time period.

[0048] Furthermore, if the remaining power of the battery 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.

[0049] Furthermore, the battery remaining power calculation process includes:

[0050] If the battery is in a charging state, the actual charge capacity is obtained according to the current charging power and the time interval, and the remaining capacity of the battery is obtained according to the remaining capacity of the battery at the previous moment and the actual charge capacity;

[0051] If the battery is in a discharging state, the discharge capacity is obtained according to the current discharge power and the time interval, the actual discharge capacity is obtained according to the discharge capacity, and the remaining capacity of the battery is obtained according to the remaining capacity of the battery at the previous moment and the actual discharge capacity.

[0052] Furthermore, the energy storage effect calculation process includes:

[0053] The operation constraints include SOC value constraints, charging power constraints and discharging power constraints; wherein the SOC value is the ratio of the remaining power of the battery to the maximum capacity of the battery;

[0054] Obtaining the current SOC value according to the operating constraint condition;

[0055] Calculating an SOC deviation according to the current SOC value and the expected SOC value;

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

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

[0058] 1. Solar street lights need to be charged efficiently under different lighting conditions to maximize the use of solar energy. The system obtains voltage error, power error and light intensity prediction values ​​in real time, 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 to ensure that the system can achieve the best charging effect under variable lighting conditions, thereby improving the system adaptability and energy efficiency.

[0059] 2. During 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 the power demand in different periods to adjust the discharge strategy to ensure that the battery will not be over-discharged. The system can not only respond to changes in power demand in real time, but also ensure that the street lights can continue to work 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 solar street light charge and discharge management system. The system calculates the remaining battery power in real time through the battery management unit, and evaluates the energy storage effect by combining the charging power, discharging power and operating constraints. The system can not only effectively optimize the battery charge and discharge process, but also report the energy storage effect in time for preventive maintenance and adjustment strategies, thereby further improving the overall reliability and adaptability of the solar street light charge and discharge management system. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 A schematic diagram of a charging and discharging management system for a solar street lamp provided by an embodiment of the present invention;

[0062] Figure 2 A schematic diagram of a charging optimization unit flow chart provided by an embodiment of the present invention;

[0063] Figure 3 A schematic diagram of MPTT provided by an embodiment of the present invention;

[0064] Figure 4 A schematic diagram of a discharge optimization unit flow chart provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0065] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0066] Solar street light charge and discharge management systems are widely used in public places such as urban roads, communities, and scenic spots due to their advantages of being green, environmentally friendly, and energy-saving. Traditional solar street light charge and discharge management systems usually include solar panels, batteries, LED lamps, and control systems. Among them, solar panels are responsible for converting sunlight into electrical energy, batteries store electrical energy for use at night, and the control system is responsible for detecting light intensity, battery charge and discharge management, and lamp switch control. This system can reduce dependence on traditional power grids, reduce energy consumption and carbon emissions, and is also suitable for lighting needs in areas without electricity or remote areas.

[0067] However, the existing charging and discharging management system solutions for solar street lights have some defects that cannot be ignored. First, when the lighting conditions are unstable, such as cloudy days or uneven lighting, it is difficult for the system to maintain efficient operation, often resulting in energy waste or low battery power. In addition, as the battery ages, the system's ability to monitor and manage the battery status is weak, further affecting the overall management effect of the system. Most of the existing control systems use a relatively simple charging and discharging 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 effects. Therefore, there is an urgent need for a new system that can more intelligently optimize the charging and discharging effects of solar panels, ensure that the battery always operates in the best working condition, and improve the adaptability of the system in complex environments.

[0068] See also Figures 1 to 4 The present invention provides a charging and discharging management system for solar street lamps. The solar street lamps in a certain community are used as experimental objects, aiming to improve the adaptability of the charging and discharging management system for solar street lamps. The technical solution is as follows:

[0069] like Figure 1 As shown, a charging and discharging management system for a solar street lamp includes:

[0070] The 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.

[0071] Furthermore, the residential solar street light historical data includes:

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

[0073] The battery data includes information such as charge and discharge history, charging efficiency, discharging efficiency, charging power and discharging power. This data is obtained through the previous application records of street lights in a certain community, or from the user manual of the solar street light;

[0074] Weather data includes temperature, humidity and PM2.5. Recording weather changes based on the data from the meteorological bureau in the area where a community is located helps determine the impact of the environment on the performance of batteries and photovoltaic panels, and adjusts the charging and discharging strategies.

[0075] User data includes the travel time of residential users. Properties usually install smart access control systems, monitoring equipment, or link with residents' smart home devices. Obtaining this data through properties can help the system identify residents' nighttime activities, intelligently adjust the on / off time and output power of street lights, and achieve more personalized lighting services.

[0076] The controller can directly monitor the on / off status of the solar street light and record the time of each on / off switching, so as to further optimize the working mode of the system under different time periods and different lighting conditions.

[0077] Through comprehensive analysis of illumination, battery status, user behavior and solar street light status, the charging and discharging management system of solar street lights can be helped to dynamically adjust the charging and discharging strategies, improve energy utilization, ensure that the battery always operates in the best condition, and improve the adaptability of the charging and discharging management system of solar street lights in complex environments. For example, in a residential area, most residents go home after 10 o'clock at night, and the lighting period of street lights can be automatically adjusted according to the user's travel mode to reduce energy waste.

[0078] The light intensity prediction unit is used to predict the light intensity using the SARIMA model according to the first data set to obtain a light intensity value.

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

[0080] Furthermore, weather data and light data of a certain community from 5 am to 6 pm are obtained according to the first data set, as shown in Table 1, and the data are recorded with a time step of half an hour, reflecting the light intensity value under different temperature and humidity conditions;

[0081] Table 1. Weather data and light data

[0082] date time <![CDATA[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] The light intensity value is used as the target variable, the temperature and humidity are used as the external regression variables of the model, and the first data set is split 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, and combines the autoregressive term and the moving average term to fit the change pattern of light intensity.

[0085] After the SARIMA model training is completed, the SARIMA model is used to predict the light intensity for 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] A fuzzy reasoning unit is used 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, 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.

[0087] Furthermore, the fuzzy reasoning unit comprises:

[0088] The voltage error is the difference between the voltage at the current moment and the voltage at the previous moment, expressed as:

[0089] ΔV=V(t)-V(t-1);

[0090] Where 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 the difference between the power at the current moment and the power at the previous moment, expressed as:

[0092] ΔP=P(t)-P(t-1);

[0093] Where 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 power is the product of the current voltage and the current, expressed as:

[0095] P(t)=V(t)×I(t);

[0096] Where, 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, thereby changing the operating voltage of the solar panel, which is expressed as:

[0098] ΔD=f(ΔP,ΔV,S);

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

[0100] The representation methods 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;

[0101] Among them, Zadeh notation is a membership representation method. In a fuzzy set, the membership of each element varies between [0,1] to represent the degree of membership of the element to the set. A membership of 1 means that the element completely belongs to the set, a membership of 0 means that it does not belong to the set at all, and a value between 0 and 1 means partial membership;

[0102] Specifically, within the first fuzzy set, the Zadeh representation is used to represent the concept of the voltage error "fifth voltage". It is assumed that the membership of the voltage error of 2 is 0.2, the membership of 4 is 0.6, and the membership of 6 is 0.9, which means that when the voltage error is 6, the membership of the concept of "fifth voltage" is the largest, and when the voltage error is 2, the membership is lower.

[0103] 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;

[0104] Among them, the Cauchy membership function is a membership function with smooth transition characteristics. It uses the Cauchy distribution to represent the membership in the fuzzy set, which is expressed as:

[0105]

[0106] Among them, f(x) is the membership value, x is the input variable; c is the center value, and a is the parameter that controls the speed of membership change.

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

[0108] Charging optimization unit, such as Figure 2 As shown, it is used to construct a fuzzy inference rule base according to 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, obtain the current moment power according to the first data set and the fuzzy inference rule base, and obtain the charging power according to the current moment power;

[0109] Among them, the current power is the maximum power point (MPP), such as Figure 3 As shown, through maximum power point tracking (MPPT), the system can adjust the working state of the solar panel in real time to ensure that the maximum power can be extracted under any environmental conditions.

[0110] Furthermore, the fuzzy set includes:

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

[0112] The first voltage indicates that the voltage is too low, the second voltage indicates that the voltage is low, the third voltage indicates that the voltage is normal, the fourth voltage indicates that the voltage is high, and the fifth voltage indicates that the voltage is too high;

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

[0114] The first power indicates that the power is too low, the second power indicates that the power is low, the third power indicates that the power is normal, the fourth power indicates that the power is high, and the fifth power indicates that the power is 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] The first intensity indicates a very strong light intensity, the second intensity indicates a relatively strong light intensity, the third intensity indicates a moderate light intensity, the fourth intensity indicates a relatively weak light intensity, and the fifth intensity indicates a very weak light intensity.

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

[0118] The first increase value indicates that the voltage adjustment amount increases a lot, the second increase value indicates that the voltage adjustment amount increases a little, the third increase value indicates that the voltage adjustment amount remains unchanged, the fourth increase value indicates that the voltage adjustment amount decreases a little, and the fifth increase value indicates that the voltage adjustment amount decreases 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 method using specific numerical values ​​may be too rigid in some cases and difficult to cope with changing environmental conditions. The use of these fuzzy sets helps the system to make flexible adjustments according to actual conditions. For example, a voltage error of -4V is not an absolute low value, but in certain situations, such as when the light intensity is very weak, it can be classified as a "first voltage", thereby triggering an appropriate adjustment strategy based on the fuzzy reasoning rule base. Through this hierarchical method, the system can intelligently classify and make decisions on complex environmental signals without relying on absolute numerical values, thereby improving the adaptability of charging and discharging management of solar street lights.

[0120] Furthermore, the fuzzy reasoning rule base includes:

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

[0122] Output variables include voltage adjustment;

[0123] Associating the fuzzy state of the input variable with the fuzzy state of the output variable to obtain a membership function of a fourth fuzzy set, and generating a fuzzy inference rule base according to the membership function of the fourth fuzzy set;

[0124] 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 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.

[0125] Table 2. Part of the fuzzy reasoning rule base

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

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

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

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

[0132] In the charge and discharge management system of solar street lights, the combination of inference rule base and maximum power point tracking technology can ensure that solar panels can always extract maximum power under various lighting conditions. Whether it is sunny or cloudy, the system can adjust the working status of the panels in real time to maximize the conversion effect of solar energy, thereby improving energy utilization and improving the adaptability of charge and discharge management of solar street lights.

[0133] Furthermore, the power acquisition process at the current moment includes:

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

[0135] 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, the membership corresponding to the voltage error, the power error and the light intensity value are respectively calculated using a Cauchy-type membership function;

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

[0137] Fuzzy state V L 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 V L 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 V L LL NM HH VH Center value c 100 300 500 800 1000 Control parameter a 100 100 100 100 100

[0142] According to the first data set, the power of a solar street light in a certain community is 24W, the voltage is 12V, and the average daytime light intensity is about 600W / m 2 , so the membership function intervals of the fuzzy sets are shown in Tables 3 to 5, where each column represents the central value c and fuzzy range control parameter a of a fuzzy state.

[0143] Perform reasoning based on the membership degree and the fuzzy reasoning rule base to obtain a fuzzy output;

[0144] The fuzzy output is defuzzified using a centroid method to obtain a voltage adjustment amount, and the current moment power is obtained according to the voltage adjustment amount, the current moment voltage, and the current moment current.

[0145] Among them, the centroid method is one of the commonly used defuzzification methods in fuzzy control systems, which is used to convert fuzzy output into precise values, expressed as:

[0146]

[0147] Among them, z * is the exact output value after defuzzification; 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 a weighted integral, which means the weighted average of all possible output values ​​z; the denominator is the total area of ​​the membership function, ensuring 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 value 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 membership degree in the "first intensity" set is 0.9. According to the fuzzy reasoning rule base, the voltage adjustment amount is the "first added value". The fuzzy output "first added value" is defuzzified by the centroid method, and the voltage adjustment amount is 3V. Assuming that the current voltage is 10V and the current is 1.5A, the adjusted power is 19.5W. The charging optimization unit can effectively handle voltage errors, power errors and light intensity, accurately adjust the voltage to optimize the power output, and enable the system to provide stable lighting under different environmental conditions, thereby improving the adaptability of charging and discharging management of solar street lights.

[0149] Furthermore, 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 nighttime initial 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, 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 the night; w1 is the first discount factor, which is used to reduce the brightness and save power, and is set to 0.8;

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

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

[0159] Wherein, t4 is the lower limit of the late night time; w2 is the second discount factor, which is used to further save power and is set to 0.6; if the time period is the daytime time period, the discharge power is zero, which is expressed as:

[0160] p out (t)=0,t∈[t4,t1];

[0161] transforming the discharge power using an S-shaped transition function at the beginning and end of the early night time period, the mid night time period, the late night time period, and the day time period;

[0162] Among them, the S-type transition function can make the time period transition smoother, which is expressed as:

[0163]

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

[0165] Specifically, the discharge time period is divided according to user data, weather data and light intensity. Table 6 shows an example of the discharge time period division in April in a certain community. It is worth noting that the starting and ending points of the discharge time period are not fixed, but are determined by weather data and light intensity values. If the visibility is low during the day, that is, the humidity is greater than 90% or the PM2.5 is greater than 150μg / m 3 Or the light intensity is less than 500W / m 2 , it still discharges.

[0166] Table 6. Discharge time 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] By setting the discount factor in different time periods through the discharge optimization unit, long-term high-power continuous discharge is avoided, reducing the load on the battery. At the same time, the introduction of the S-type transition function makes the adjustment of the discharge power smoother, 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 days, rainy days, etc.), the system can automatically judge and appropriately extend the discharge time at night to ensure that sufficient lighting is still provided under low visibility conditions, increase the safety of community roads, especially the protection of pedestrians at night, thereby improving the adaptability of the charging and discharging management of solar street lights.

[0169] Furthermore, if the remaining power of the battery is less than a fixed threshold, the discharge power is the product of the discharge power at the previous moment and the third discount factor, expressed as:

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

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

[0172] Specifically, assume that the initial power of a solar street light battery is 50%. In the early night time period, the system discharges according to the set load demand power. As time goes by, the remaining power of the battery gradually decreases in the late night time period. When the power drops to the SOC value of 30%, the system automatically starts the protection mechanism and uses the third discount factor to adjust the discharge power in the late night. This adjustment not only effectively extends the discharge time, especially in rainy days or continuous cloudy days when there is insufficient light, to prevent lighting interruptions caused by power exhaustion, but also avoids damage to the battery due to excessive discharge, thereby improving the charging and discharging management adaptability and battery protection capabilities of solar street lights.

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

[0174] Set the initial battery charge to 0;

[0175] If the battery is in a charging state, the actual charge capacity is obtained according to the current charging power, charging efficiency and time interval, and the remaining battery capacity is obtained according to the remaining battery capacity at the previous moment and the actual charge capacity, which is expressed as:

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

[0177] Among them, 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 capacity is obtained according to the current discharge power and the time interval, the actual discharge capacity is obtained according to the discharge capacity and the discharge efficiency, and the remaining capacity of the battery is obtained according to the remaining capacity of the battery at the previous moment and the actual discharge capacity, which is expressed as:

[0179]

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

[0181] Among them, the charging efficiency w charge and discharge efficiency w discharge Calculated from the first data set, or obtained according to the solar street light safety manual;

[0182] Specifically, by accurately calculating the change in power during charging and discharging, the charging and discharging management system of solar street lights can optimize the use of batteries and improve energy utilization. The system adjusts strategies 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 panels receive less light, and the system can automatically adjust the charging power to ensure that charging is carried out under the best possible conditions. In addition, the system can effectively prevent the battery power from being too low, thereby preventing the solar street lights from failing to work properly at night or on cloudy days, and improving the reliability and adaptability of the system. For example, if the power is close to the minimum at night, the system will automatically reduce the discharge power to ensure that the lights will not go out due to insufficient power.

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

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

[0185] Among them, the SOC value constraint is expressed as:

[0186] S min ≤SOC(t)≤Smax ;

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

[0188] Among them, the SOC value at time t is expressed as:

[0189]

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

[0191] The charging power constraint is expressed as:

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

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

[0194] The discharge power constraint is expressed as:

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

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

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

[0198]

[0199] Among them, C soc is the energy storage effect; S targetThe SOC expected value is set to 80% when charging during the day and to 20% when used at night; in addition, by multiplying the difference by 2, the range of the balance index can be adjusted to the interval of [-1,1], which can intuitively reflect the degree of deviation between the actual SOC value and the expected SOC value.

[0200] Specifically, in the charge and discharge management system of solar street lights, the energy storage effect refers to optimizing the charge and discharge management of batteries to ensure that their power is maintained within a reasonable range, thereby achieving efficient use of energy and extending battery life. Through real-time calculation of the energy storage effect, the system can report the energy storage effect in a timely manner for preventive maintenance and adjustment strategies. For example, when the SOC value is lower than the expected SOC value during the day, the system will increase the charging power to increase the battery power; conversely, when the SOC value is higher than the expected SOC value during the day, the system will reduce the charging power or increase the discharging power to avoid overcharging the battery, which not only reduces battery damage caused by over-discharge or overcharging, but also improves the overall reliability and adaptability of the system.

[0201] In summary, by using the SARIMA model to predict the light intensity through the light intensity prediction unit, the system can obtain future light conditions in advance, and based on the prediction results, fuzzy reasoning unit and charging optimization unit, the system can optimize the voltage adjustment and charging strategy to achieve the best charging effect under various lighting conditions. The discharge optimization unit intelligently divides the discharge period according to historical data and dynamically adjusts the discharge power according to actual needs. This can not only effectively avoid excessive discharge of the battery, but also ensure the stable operation of the street light at night or in low light conditions. The battery management unit dynamically calculates the remaining battery power and combines the charging and discharging power to ensure that the battery will not be overcharged or over-discharged during the charging and discharging process, effectively extending the battery life and improving the energy storage effect, thereby improving the adaptability of charging and discharging management of solar street lights.

[0202] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A charging and discharging management system for solar street lamps, characterized in that: include: A data acquisition unit, used to acquire historical data of residential solar street lights, and pre-process the historical data of residential solar street lights to obtain a first data set; A light intensity prediction unit, configured to predict the light intensity using a SARIMA model according to the first data set to obtain a light intensity value; a fuzzy reasoning unit, configured to obtain, according to 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 according to 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, obtain the current moment power according to the first data set and the fuzzy inference rule base, and obtain the charging power according to the current moment power; a discharge optimization unit, configured to divide the discharge time period according to the first data set to obtain the discharge power; The battery management unit is used to calculate the remaining power of the battery according to the charging power and the discharging power, and obtain the energy storage effect according to the remaining power of the battery using the operation constraint conditions.

2. A solar street light charge and discharge management system according to claim 1, characterized in that: The residential solar street light historical data includes: Light data includes sunlight intensity and daily sunlight duration; The battery data includes charge and discharge history data, charging efficiency, discharging efficiency, the charging power and the discharging power; Weather data includes temperature, humidity and PM2.5; User data include travel times for residential users; The on / off state of the solar street light.

3. A solar street light charge and discharge management system according to claim 1, characterized in that: The fuzzy reasoning unit comprises: 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 current power is the product of the current voltage and the current; The representation methods 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 current moment power acquisition process includes: According to the first data set, the voltage error, the power error and the light intensity value are fuzzified using Zadeh representation 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, the membership corresponding to the voltage error, the power error and the light intensity value are calculated respectively; Perform reasoning based on the membership degree and the fuzzy reasoning rule base to obtain a fuzzy output; The fuzzy output is defuzzified using a centroid method to obtain a voltage adjustment amount, and the current moment power is obtained according to the voltage adjustment amount, the current moment voltage, and the current moment current.

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

6. A solar street light charge and discharge management system according to claim 1, characterized in that: The fuzzy inference rule base includes: The input variables include the voltage error, the power error and the light intensity value; Output variables include voltage adjustment; Associating the fuzzy state of the input variable with the fuzzy state of the output variable to obtain a membership function of a fourth fuzzy set, and generating a fuzzy inference rule base 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 comprises: 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; If the time period is the nighttime initial 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 the 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 the second discount factor; If the time period is the daytime time period, the discharge power is zero; The discharge power is shifted using an S-shaped transition function at the beginning and end of the early night time period, the mid night time period, the late night time period, and the day 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 comprises: If the battery is in a charging state, the actual charge capacity is obtained according to the current charging power and the time interval, and the remaining capacity of the battery is obtained according to the remaining capacity of the battery at the previous moment and the actual charge capacity; If the battery is in a discharging state, the discharge capacity is obtained according to the current discharge power and the time interval, the actual discharge capacity is obtained according to the discharge capacity, and the remaining capacity of the battery is obtained according to the remaining capacity of the battery 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 operation constraints include SOC value constraints, charging power constraints and discharging power constraints; wherein the SOC value is the ratio of the remaining power of the battery to the maximum capacity of the battery; Obtaining the current SOC value according to the operating constraint condition; Calculating an SOC deviation according to the current SOC value and the expected SOC value; The SOC deviation is normalized according to the operating constraint condition to obtain the energy storage effect.

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