Photovoltaic street lamp self-recovery power supply and energy storage management method

Abstract

The application discloses a photovoltaic street lamp self-recovery power supply and energy storage management method, relates to the field of public lighting, and solves the problem of poor application effect of the existing photovoltaic street lamp self-recovery power supply and energy storage management method, and comprises the following steps: S1, real-time light monitoring is performed on a target lighting area, the target lighting area is divided into lighting time periods according to a monitoring result, and lighting time period division data is obtained; S2, power supply strategies are formulated for target photovoltaic street lamps according to the lighting time period division data, illumination intensity matching and cloud thickness matching are performed on energy storage prediction periods, real-time energy storage periods and strategy execution historical time periods, and historical period matching data is obtained; and S3, energy consumption fitting equations are created by analyzing the prediction period matching data to adjust the energy storage power of the target photovoltaic street lamps, and the service life of the photovoltaic street lamps can be prolonged.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of public lighting, and relates to photovoltaic power generation technology, in particular to a photovoltaic street lamp self-recovery power supply and energy storage management method. BACKGROUND

[0002] The existing photovoltaic street lamp self-recovery power supply and energy storage management method has the following defects when performing energy storage management:

[0003] 1. The existing photovoltaic street lamp self-recovery power supply method relies on a timing device to control the activation of the photovoltaic street lamp, and does not divide the power generation period of the photovoltaic street lamp into a street lamp energy storage period, a hybrid power supply period and a direct power supply period in combination with the environmental brightness and the power generation power of the photovoltaic power generation panel, thereby resulting in low power utilization efficiency of the photovoltaic street lamp.

[0004] 2. The existing photovoltaic street lamp energy storage method can only charge according to a fixed charging power to a set upper limit value of charging when performing photovoltaic charging, and cannot automatically adjust the charging power and the upper limit value of charging according to the predicted power consumption of future periods, so it is difficult to realize shallow charging and shallow discharging of the energy storage device, thereby resulting in a decrease in the cycle life of the energy storage device and affecting the service life of the photovoltaic street lamp.

[0005] Therefore, the application provides a photovoltaic street lamp self-recovery power supply and energy storage management method. SUMMARY

[0006] In view of the deficiencies of the prior art, the application aims to provide a photovoltaic street lamp self-recovery power supply and energy storage management method, and aims to improve the power utilization efficiency and service life of the photovoltaic street lamp.

[0007] In order to achieve the above-mentioned purpose, the application adopts the following technical scheme: a photovoltaic street lamp self-recovery power supply and energy storage management method comprising the following steps:

[0008] Step S1: obtaining a target photovoltaic street lamp, setting a lighting area corresponding to the target photovoltaic street lamp as a target lighting area, performing real-time light monitoring on the target lighting area, dividing the lighting period of the target lighting area according to the monitoring result, and obtaining lighting period division data;

[0009] Step S2: formulating a self-recovery power supply strategy for the target photovoltaic street lamp according to the lighting period division data, obtaining an energy storage prediction period and a real-time energy storage period, performing light intensity matching and cloud thickness matching between the real-time energy storage period and a strategy execution history period to obtain real-time period matching data, performing light intensity matching and cloud thickness matching between the energy storage prediction period and the strategy execution history period to obtain prediction period matching data, and obtaining history period matching data;

[0010] Step S3: create a power consumption fitting equation by analyzing the predicted period matching data, and adjust the energy storage power of the target photovoltaic street lamp in the street lamp energy storage period.

[0011] Further, in the step S1, the following steps are specifically included:

[0012] Step S11: acquire the photovoltaic street lamp to obtain a target photovoltaic street lamp, and set the street lighting area corresponding to the target photovoltaic street lamp as a target lighting area;

[0013] Step S12: set a regional environment monitoring period, and perform environment monitoring on the target lighting area in the regional environment monitoring period; divide the regional environment monitoring period into a street lamp energy storage period, a hybrid power supply period and a direct power supply period to obtain lighting period division data.

[0014] Further, in the step S12, the following steps are specifically included:

[0015] Step S121: real-time monitor the environment brightness of the target lighting area to obtain an environment real-time brightness value, and set a lighting brightness preset value; if the environment real-time brightness value is less than or equal to the lighting brightness preset value, set the acquisition time point corresponding to the environment real-time brightness value as a lighting opening time point; if the environment real-time brightness value is greater than the lighting brightness preset value, set the acquisition time point corresponding to the environment real-time brightness value as a lighting closing time point; obtain a plurality of lighting opening time points and a plurality of lighting closing time points.

[0016] Step S122: set the period from the lighting opening time point to the lighting closing time point as a street lamp lighting period, and set the period from the lighting closing time point to the lighting opening time point as a street lamp energy storage period; set a sliding time window to perform power monitoring iteration on each street lamp lighting period, and according to the monitoring result, divide the sliding time window into a complete lighting window and a non-complete lighting window; set the period covered by the non-complete lighting window as a hybrid power supply period, and set the period covered by the complete lighting window as a direct power supply period.

[0017] The step S122 specifically includes the following steps:

[0018] Acquire the real-time power generated by the street lamp photovoltaic panel in the sliding time window to obtain a window real-time power generation, acquire the street lamp lighting power corresponding to the sliding time window to obtain a window real-time lighting power; if the window real-time power generation is greater than the window real-time lighting power, set the sliding time window as a complete lighting window; if the window real-time power generation is less than or equal to the window real-time lighting power, set the sliding time window as a non-complete lighting window.

[0019] Further, the step S2 specifically comprises the following steps:

[0020] Step S21: Obtain lighting period division data, and formulate a segmented power supply strategy for the target photovoltaic street lamp in the street lamp energy storage period, hybrid power supply period and direct power supply period according to the lighting period division data;

[0021] Step S22: Obtain a plurality of historical working cycles of the target photovoltaic street lamp using the segmented power supply strategy for self-recovery power supply;

[0022] Step S23: If the current time is in the street lamp energy storage period, set the natural date in which the current time is located as a real-time energy storage cycle, if the current time is not in the street lamp energy storage period, set the next natural date corresponding to the current natural date as a real-time energy storage cycle, and set the next natural date corresponding to the real-time energy storage cycle as an energy storage prediction cycle;

[0023] Step S24: Match the real-time energy storage cycle and the plurality of historical working cycles with light conditions, and select a plurality of lighting matching cycles from the historical working cycles according to the matching result to obtain real-time cycle matching data;

[0024] Step S25: Match the energy storage prediction cycle and the plurality of historical working cycles with light conditions, and select a plurality of prediction matching cycles from the historical working cycles according to the matching result to obtain prediction cycle matching data.

[0025] Further, the step S21 specifically comprises the following steps:

[0026] Obtain the street lamp energy storage period, the hybrid power supply period and the direct power supply period;

[0027] If the target photovoltaic street lamp is in the street lamp energy storage period, the photovoltaic street lamp is not powered for lighting, if the target photovoltaic street lamp is in the hybrid power supply period, the photovoltaic street lamp is preferentially powered for lighting using photovoltaic output power, and the energy storage device is used to supplement power if the power is insufficient, and if the target photovoltaic street lamp is in the direct power supply period, the photovoltaic street lamp is directly powered for lighting using the energy storage device.

[0028] Further, the step S24 specifically comprises the following steps:

[0029] Step S241: Randomly select a sample historical cycle in the historical working cycle, match the real-time energy storage cycle and the sample historical cycle to the light intensity time axis, and set a light matching window on the light intensity time axis;

[0030] Step S242: setting a real-time time segment corresponding to the light matching window in the real-time energy storage period as a first light window segment, setting a real-time time segment corresponding to the light matching window in the sample history period as a second light window segment, acquiring a segment light intensity corresponding to the first light window segment of the target photovoltaic street lamp to obtain a first segment light intensity, acquiring a historical light intensity corresponding to the second light window segment of the target photovoltaic street lamp to obtain a second segment light intensity, calculating a difference between the first segment light intensity and the second segment light intensity, and calculating a ratio of an absolute value of the obtained difference to the second segment light intensity to obtain a light intensity deviation corresponding to the light matching window;

[0031] Step S243: setting a light deviation preset interval, if the light intensity deviation is in the light deviation preset interval, setting the light matching window as a light effective matching window, and if the light intensity deviation is not in the light deviation preset interval, setting the light matching window as a light ineffective matching window.

[0032] Further, in the step S24, the following steps are specifically included:

[0033] Step S244: using the light matching window to slide through the light intensity time axis, performing time length statistics on the light effective matching windows existing in the light intensity time axis to obtain an effective light matching time length, performing time length acquisition on the light intensity time axis to obtain a light monitoring cumulative time length, and calculating a ratio of the effective light matching time length to the light monitoring cumulative time length to obtain a light matching degree corresponding to the sample history period;

[0034] Step S245: acquiring a cloud layer matching window and a cloud layer thickness time axis, using the cloud layer matching window to traverse the cloud layer thickness time axis to obtain a cloud layer matching degree corresponding to the sample history period;

[0035] Step S246: acquiring the cloud layer matching degree and the light matching degree corresponding to each historical working period;

[0036] Step S247: setting a cloud layer reference matching degree and a light reference matching degree, screening a historical working period with a cloud layer matching degree greater than or equal to the cloud layer reference matching degree and a light matching degree greater than or equal to the light reference matching degree as an illumination matching period to obtain real-time period matching data.

[0037] Further, in the step S3, the following steps are specifically included:

[0038] Step S31: according to the real-time period matching data, performing coordinate representation on a street lamp illumination power consumption and a night length proportion corresponding to each illumination matching period to obtain a plurality of coordinate scatter points;

[0039] Step S32: If the marked multiple coordinate scatter points are in a linear correlation, the night length proportion is linearly fitted with the street lamp lighting power consumption, and if the marked multiple coordinate scatter points are not in a linear correlation, the night length proportion is polynomially fitted with the street lamp lighting power consumption, to obtain a power consumption fitting equation;

[0040] Step S33: According to the power consumption fitting equation, the energy storage power of the target photovoltaic street lamp in the street lamp energy storage period is adjusted.

[0041] Further, in step S31, the following steps are specifically included:

[0042] According to the real-time period matching data, a plurality of lighting matching periods are obtained, the sunrise time and the sunset time corresponding to the lighting matching period are obtained, the difference between the sunset time and the sunrise time is calculated, and the ratio of the obtained difference to 24 is obtained, to obtain the night length proportion corresponding to each lighting matching period;

[0043] The lighting power consumption of the target photovoltaic street lamp in the lighting matching period is obtained, to obtain a plurality of street lamp lighting power consumptions;

[0044] The obtained multiple night length proportions are sequentially named as Y1 night length proportion to Ya night length proportion according to the values from small to large, the street lamp lighting power consumptions corresponding to Y1 night length proportion to Ya night length proportion are set as Y1 street lamp lighting power consumption to Ya street lamp lighting power consumption, and the coordinate scatter points corresponding to Y1 night length proportion to Ya night length proportion as the abscissa and Y1 street lamp lighting power consumption to Ya street lamp lighting power consumption as the ordinate are marked in the plane rectangular coordinate system.

[0045] Further, in step S33, the following steps are specifically included:

[0046] The night length proportion corresponding to the energy storage prediction period is obtained, to obtain a prediction period night length proportion, the prediction period night length proportion is substituted into the power consumption fitting equation, to obtain a period prediction power consumption;

[0047] The period energy storage corresponding to each prediction matching period is obtained, and the average of the obtained period energy storages is calculated, to obtain a period effective energy storage corresponding to the energy storage prediction period, if the period effective energy storage is greater than or equal to the period prediction power consumption, the energy storage upper limit value corresponding to the street lamp energy storage period is set as an energy storage optimization upper limit value, and if the period effective energy storage is less than the period prediction power consumption, the energy storage upper limit value corresponding to the street lamp energy storage period is set as an energy storage physical upper limit value;

[0048] The battery capacity corresponding to the target photovoltaic street lamp at the current time is obtained, to obtain a real-time battery capacity, and the time interval between the current time and the end time point of the street lamp energy storage period is obtained, to obtain an available charging duration;

[0049] The available charging duration, real-time battery capacity and upper limit value of energy storage are calculated to obtain an energy storage preset power, and the energy storage power corresponding to the target photovoltaic street lamp is adjusted to the energy storage preset power.

[0050] In summary, due to the adoption of the above technical solutions, the present application has the following advantages:

[0051] 1. The present application divides the power generation period of the photovoltaic street lamp into a street lamp energy storage period, a hybrid power supply period and a direct power supply period by combining the ambient brightness and the power generation power of the photovoltaic power generation panel, thereby improving the electric energy utilization efficiency of the photovoltaic street lamp.

[0052] 2. The present application predicts the power consumption of the future period by analyzing the cloud thickness and the light intensity of the future period, and automatically adjusts the charging power and the upper limit value of the charging by analyzing the prediction results, so as to realize shallow charging and shallow discharging of the energy storage device, thereby improving the cycle life of the energy storage device and providing protection for the service life of the photovoltaic street lamp. BRIEF DESCRIPTION OF DRAWINGS

[0053] In order to facilitate understanding by those skilled in the art, the present application will be further described below with reference to the accompanying drawings.

[0054] Figure 1 is the implementation step diagram of the present application;

[0055] Figure 2 is the target lighting area diagram of the present application;

[0056] Figure 3 is the light matching window diagram of the present application;

[0057] Wherein, 1 is the target lighting area, 2 is the light matching window, and 3 is the light intensity time axis. DETAILED DESCRIPTION

[0058] The technical solutions of the present application will be described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0059] Embodiment one

[0060] Please refer to Figure 1 The present application provides a technical solution: a photovoltaic street lamp self-recovery power supply and energy storage management method, comprising the following steps:

[0061] Step S1: Real-time light monitoring of the target lighting area is performed, and the target lighting area is divided into lighting periods according to the monitoring results to obtain lighting period division data.

[0062] The step S1 specifically comprises the following steps:

[0063] Please refer to Figure 2 The target street lamp corresponding to the target lighting area is obtained, and the target lighting area is set as the target lighting area;

[0064] It should be noted here that:

[0065] In this application, the photovoltaic street lamp referred to here is specifically an off-grid photovoltaic street lamp, which operates completely independently of the power grid, generates electricity through a photovoltaic panel, stores energy in a battery, and is managed by a controller to achieve night lighting.

[0066] Each natural date is set as a regional environmental monitoring period, and the target lighting area in the regional environmental monitoring period is monitored, and the regional environmental monitoring period is divided into a street lamp energy storage period, a hybrid power supply period and a direct power supply period according to the monitoring result, and lighting period division data is obtained;

[0067] Specifically as follows:

[0068] The target lighting area is monitored in real time, and the environmental real-time brightness value is obtained, and a lighting brightness preset value is set, if the environmental real-time brightness value is less than or equal to the lighting brightness preset value, the acquisition time point corresponding to the environmental real-time brightness value is set as the lighting opening time point, if the environmental real-time brightness value is greater than the lighting brightness preset value, the acquisition time point corresponding to the environmental real-time brightness value is set as the lighting closing time point, and a plurality of lighting opening time points and a plurality of lighting closing time points are obtained;

[0069] It should be noted here that:

[0070] In this application, the lighting brightness preset value referred to here is specifically the environmental brightness value when the street lamp lighting needs to be turned on.

[0071] Any one of the lighting opening time point is extended to the lighting closing time point, and the period is set as the street lamp lighting period, and any one of the lighting closing time point is extended to the lighting opening time point, and the period is set as the street lamp energy storage period, and a sliding time window is set to monitor each street lamp lighting period, and according to the monitoring result, the sliding time window is divided into a complete lighting window and a non-complete lighting window, the non-complete lighting window is set as a hybrid power supply period, and the complete lighting window is set as a direct power supply period;

[0072] It should be noted here that:

[0073] The street light illumination period referred to herein includes a hybrid power supply period and a direct power supply period.

[0074] Specifically as follows:

[0075] In the process of monitoring the power generation of the target photovoltaic street lamp, a sliding time window is set, the real-time power generation of the photovoltaic panel of the street lamp in the sliding time window is obtained, the window real-time power generation is obtained, the street lamp illumination power of the target photovoltaic street lamp in the sliding time window is obtained, the window real-time illumination power is obtained, if the window real-time power generation is greater than the window real-time illumination power, the sliding time window is set as a full illumination window, and if the window real-time power generation is less than or equal to the window real-time illumination power, the sliding time window is set as a non-full illumination window.

[0076] Step S2: According to the illumination period division data, the self-recovery power supply strategy of the target photovoltaic street lamp is formulated, the energy storage prediction period and the real-time energy storage period are obtained, the real-time energy storage period is matched with the illumination intensity and the cloud layer thickness of the strategy execution history period to obtain real-time period matching data, the energy storage prediction period is matched with the illumination intensity and the cloud layer thickness of the strategy execution history period to obtain prediction period matching data, and the history period matching data is obtained.

[0077] In the step S2, the following steps are specifically included:

[0078] The illumination period division data is obtained, and the segmented power supply strategy of the target photovoltaic street lamp in the street lamp energy storage period, the hybrid power supply period and the direct power supply period is formulated according to the illumination period division data;

[0079] Specifically as follows:

[0080] The street lamp energy storage period, the hybrid power supply period and the direct power supply period are obtained according to the illumination period division data respectively.

[0081] If the target photovoltaic street lamp is in the street lamp energy storage period, the photovoltaic street lamp is not illuminated and powered, if the target photovoltaic street lamp is in the hybrid power supply period, the photovoltaic output power is preferentially used to illuminate and power the photovoltaic street lamp, and the energy storage device is used to supplement the power if the power is insufficient, and if the target photovoltaic street lamp is in the direct power supply period, the energy storage device is directly used to illuminate and power the photovoltaic street lamp.

[0082] It should be noted here that:

[0083] In this application, the photovoltaic output power referred to herein is specifically the real-time power generated by the photovoltaic module to illuminate and power the target photovoltaic street lamp.

[0084] Obtain a plurality of historical working periods by acquiring a historical working period of self-recovery power supply using a segmented power supply strategy for a target photovoltaic street lamp;

[0085] It should be noted here that:

[0086] In this application, the historical working period referred to here includes the street lamp energy storage period, the hybrid power supply period and the direct power supply period corresponding to a complete normal day;

[0087] If the current time is in the street lamp energy storage period, the natural day in which the current time is located is set as the real-time energy storage period, and if the current time is not in the street lamp energy storage period, the next natural day corresponding to the current natural day is set as the real-time energy storage period, and the next natural day corresponding to the real-time energy storage period is set as the energy storage prediction period;

[0088] It should be noted here that:

[0089] In this application, since real-time self-recovery power supply and energy storage are required in this application, if the street lamp energy storage period corresponding to the target photovoltaic street lamp is 6:00-18:00, and the current time is 19:00, the current time is not in the street lamp energy storage period, since the street lamp energy storage period has already passed, it is impossible to adjust the power in the street lamp energy storage period, so the next day (the next natural day is set as the energy storage prediction period).

[0090] Match the real-time energy storage period and the plurality of historical working periods with the light conditions, and select a plurality of lighting matching periods from the plurality of historical working periods according to the matching results to obtain real-time period matching data;

[0091] Specifically as follows:

[0092] Please refer to Figure 3 , randomly select a sample historical period from the plurality of historical working periods, match the real-time energy storage period and the sample historical period to the light intensity time axis, and set a light matching window on the light intensity time axis;

[0093] It should be noted here that:

[0094] Here, the sample historical period is selected as a sample, and the random selection method is used here. The analysis process of the sample historical period will be repeated in the future. Here, it is only used as an example, so there is no requirement for the selection process;

[0095] In this application, the time length corresponding to the light intensity time axis is consistent with the real-time energy storage period and the historical working period, which is 24 hours;

[0096] In the present application, the light matching window referred to herein is specifically a time segment capable of sliding on the light intensity time axis;

[0097] In the present application, the duration of the light matching window referred to herein is set to 5 minutes.

[0098] The real-time time segment corresponding to the light matching window in the real-time energy storage period is set as a first light window segment, and the real-time time segment corresponding to the light matching window in the sample history period is set as a second light window segment. The segment light intensity corresponding to the first light window segment of the target photovoltaic street lamp is obtained by the meteorological device to obtain the first segment light intensity. The historical light intensity corresponding to the second light window segment of the target photovoltaic street lamp is obtained to obtain the second segment light intensity. The difference between the first segment light intensity and the second segment light intensity is calculated, and the ratio of the absolute value of the obtained difference to the second segment light intensity is calculated to obtain the light intensity deviation corresponding to the light matching window.

[0099] It should be noted here that:

[0100] In the present application, the segment light intensity referred to herein is specifically the average light intensity of the period.

[0101] A light deviation preset interval is set. If the light intensity deviation is in the light deviation preset interval, the light matching window is set as a light effective matching window. If the light intensity deviation is not in the light deviation preset interval, the light matching window is set as a light ineffective matching window.

[0102] It should be noted here that:

[0103] In the present application, the lower limit of the light deviation preset interval is 0, i.e. there is no deviation between the first segment light intensity and the second segment light intensity. The light matching window matched with the history is obtained, and the light intensity deviation corresponding to each light matching window is obtained respectively. The largest light intensity deviation is set as the upper limit of the light deviation preset interval.

[0104] In the present application, the light effective matching window referred to herein includes the case where the light intensity deviation is at the boundary of the light deviation preset interval.

[0105] The light intensity time axis is slid and traversed using the light matching window, the effective light matching duration is obtained by performing duration statistics on the light effective matching window existing in the light intensity time axis, the light monitoring cumulative duration is obtained by performing duration acquisition on the light intensity time axis, and the light matching degree corresponding to the sample history period is obtained by calculating the ratio of the effective light matching duration to the light monitoring cumulative duration.

[0106] Match the real-time energy storage period and the sample historical period to the cloud thickness time axis, and set a cloud matching window on the cloud thickness time axis;

[0107] It should be noted here that:

[0108] In this application, the length of time corresponding to the cloud thickness time axis here is consistent with the real-time energy storage period and the historical working period, both of which are 24 hours;

[0109] In this application, the cloud matching window referred to here is specifically a time segment that can slide on the cloud thickness time axis;

[0110] In this application, the length of time corresponding to the cloud matching window here is set to 5 minutes.

[0111] Set the real-time time segment corresponding to the cloud matching window in the real-time energy storage period as the first cloud window segment, and set the real-time time segment corresponding to the cloud matching window in the sample historical period as the second cloud window segment. Obtain the first segment cloud thickness by the meteorological device in the first cloud window segment corresponding to the target photovoltaic street lamp, and obtain the second segment cloud thickness by the historical cloud thickness in the second cloud window segment corresponding to the target photovoltaic street lamp. Calculate the difference between the first segment cloud thickness and the second segment cloud thickness, and calculate the ratio of the absolute value of the obtained difference to the second segment cloud thickness to obtain the cloud thickness deviation corresponding to the cloud matching window;

[0112] Set a cloud deviation preset interval. If the cloud thickness deviation is in the cloud deviation preset interval, set the cloud matching window as a cloud effective matching window. If the cloud thickness deviation is not in the cloud deviation preset interval, set the cloud matching window as a cloud invalid matching window;

[0113] It should be noted here that:

[0114] In this application, the lower limit of the cloud deviation preset interval is 0, that is, there is no deviation between the first segment cloud thickness and the second segment cloud thickness. Obtain the cloud matching window that is cloud effective matching window for history matching, and obtain the cloud thickness deviation corresponding to each cloud matching window respectively. Set the largest cloud thickness deviation as the upper limit of the cloud deviation preset interval;

[0115] In this application, the cloud effective matching window referred to here includes the case where the cloud thickness deviation is at the boundary of the cloud deviation preset interval.

[0116] The cloud layer matching window is used to slide through the cloud layer thickness time axis, the time length of the effective cloud layer matching window existing in the cloud layer thickness time axis is counted, the effective cloud layer matching time length is obtained, the cloud layer thickness time axis is time length acquired, the cloud layer monitoring cumulative time length is obtained, the ratio of the effective cloud layer matching time length and the cloud layer monitoring cumulative time length is calculated, and the cloud layer matching degree corresponding to the sample historical period is obtained.

[0117] The acquisition process of the cloud layer matching degree and the illumination matching degree corresponding to the sample historical period is repeated, and the cloud layer matching degree and the illumination matching degree corresponding to each historical working period are acquired respectively.

[0118] It should be noted here that:

[0119] In this application, the cloud layer thickness acquisition device here is a laser ceilometer.

[0120] Here, the cloud layer matching window is used to slide through the cloud layer thickness time axis, the time length of the effective cloud layer matching window existing in the cloud layer thickness time axis is counted, and the effective cloud layer matching time length is obtained. This process includes two operations:

[0121] 1. Since the sample historical period and the real-time energy storage period are in time axis alignment, and the first cloud layer window segment and the second cloud layer window segment are in synchronization in the time axis, here the cloud layer thickness of the same period in different periods is matched.

[0122] For example, if the sample historical period is the first natural date, and the real-time energy storage period is the second natural date, if the first cloud layer window segment is from nine o'clock to nine thirty of the first natural date, then the second cloud layer window segment is from nine o'clock to nine thirty of the second natural date.

[0123] The above matching method can ensure the time period synchronization of the matching process;

[0124] 2. The time length of the effective cloud layer matching window is counted through the classification result of the cloud layer window segment in 1, the effective cloud layer matching time length is obtained, the cloud layer thickness time axis is time length acquired, the cloud layer monitoring cumulative time length is obtained, the ratio of the effective cloud layer matching time length and the cloud layer monitoring cumulative time length is calculated, and the cloud layer matching degree corresponding to the sample historical period is obtained. This can improve the quantitative standard of the cloud layer matching degree, and also transition the segment matching result to the period matching result, providing a basis for cloud layer matching between two periods.

[0125] The subsequent illumination matching is the same.

[0126] The cloud layer reference matching degree and the illumination reference matching degree are set respectively, the historical working period with the cloud layer matching degree greater than or equal to the cloud layer reference matching degree and the illumination matching degree greater than or equal to the illumination reference matching degree is screened as the lighting matching period, and the real-time period matching data is obtained.

[0127] It should be noted here that:

[0128] In this application, the historical lighting matching period of the energy storage management system has been completed, the cloud matching degree corresponding to each historical lighting matching period is obtained, the cloud reference matching degree is set as the minimum value, the illumination matching degree corresponding to each historical lighting matching period is obtained, and the illumination reference matching degree is set as the minimum value.

[0129] Repeat the process of obtaining real-time period matching data, match the energy storage prediction period with multiple historical working periods, and select multiple prediction matching periods from multiple historical working periods according to the matching result to obtain prediction period matching data;

[0130] It should be noted here that:

[0131] Here, the energy storage prediction period is matched with multiple historical working periods based on the intensity of illumination and the thickness of the cloud layer to obtain multiple prediction matching periods, which are as follows:

[0132] Illumination intensity is a key factor in determining the efficiency of photovoltaic panels. Under ideal conditions (no cloud cover, standard atmospheric conditions, etc.), illumination intensity is positively correlated with the output power of photovoltaic panels. That is, the stronger the illumination intensity, the more photons the photovoltaic panel receives, and the more electricity it generates; conversely, the illumination intensity decreases, and the power generation decreases. For example, at noon on a sunny day, the illumination intensity reaches its peak for the day, and the photovoltaic panel of the photovoltaic street lamp generates the most power, and the energy storage system can charge quickly; while in the morning or evening, the illumination intensity is weak, and the power generation is significantly reduced;

[0133] Cloud thickness has an important regulating effect on illumination intensity. Thin clouds have a certain scattering effect on illumination, which will weaken the illumination intensity reaching the ground, but the overall distribution is relatively uniform; while thick clouds will strongly block and absorb solar radiation, resulting in a significant reduction in ground illumination intensity, and even no illumination may occur. For example, in cloudy weather, the cloud layer changes constantly, sometimes thin clouds cover, and sometimes thick clouds accumulate, the power generation of photovoltaic panels will also fluctuate, by analyzing the change of cloud thickness in the historical working period, the stability of photovoltaic power generation under different cloud thickness can be understood, which provides a basis for predicting the power generation of the current energy storage period.

[0134] The prediction period matching data and the real-time period matching data are defined as historical period matching data;

[0135] It should be noted here that:

[0136] The historical period matching data includes predicted period matching data and real-time period matching data;

[0137] The predicted period matching data includes a time period corresponding to each predicted matching period, sunrise and sunset time, and period energy storage;

[0138] The real-time period matching data includes each lighting matching period, and a time period corresponding to each lighting matching period, sunrise and sunset time, and lighting power consumption;

[0139] It should be noted that the predicted matching period and the lighting matching period referred to herein are historical time periods.

[0140] It should be noted that:

[0141] Step S2 implements self-recovery power supply strategy and multi-dimensional period matching analysis, which can realize accurate response and intelligent optimization of the photovoltaic street lamp system to dynamic environmental conditions: the differentiated power supply strategy developed based on the lighting period division data can combine the predicted period of energy storage and the real-time period of energy storage, and dynamically calibrate the energy management parameters through historical-real-time two-dimensional matching of light intensity and cloud thickness. Real-time period matching data can immediately correct the current power supply mode to ensure that the system quickly adapts to sudden weather changes. The predicted period matching data is obtained by mining historical rules to optimize the charging and discharging strategy in the future time period in advance, thereby avoiding the risk of overcharging and overdischarging.

[0142] Step S3: creating a power consumption fitting equation based on the predicted period matching data, and adjusting the energy storage power of the target photovoltaic street lamp in the street lamp energy storage period according to the power consumption fitting equation;

[0143] The step S3 specifically includes the following steps:

[0144] Specifically as follows:

[0145] According to the real-time period matching data, a plurality of lighting matching periods are obtained, the sunrise time and the sunset time corresponding to each lighting matching period are obtained, the difference between the sunset time and the sunrise time is calculated, and the ratio of the obtained difference to 24 is obtained to obtain the night length ratio corresponding to each lighting matching period;

[0146] It should be noted that:

[0147] In the present application, the period length corresponding to each lighting matching period is 24 hours, so the ratio of the difference between the sunset time and the sunrise time to 24 is the night length ratio corresponding to the lighting matching period.

[0148] The lighting power consumption of the target photovoltaic street lamp in each lighting matching period is obtained, and a plurality of street lamp lighting power consumptions are obtained.

[0149] The obtained plurality of night length ratios are sequentially named as Y1 night length ratio to Ya night length ratio in ascending order of numerical value, the road lamp lighting electricity consumption corresponding to Y1 night length ratio to Ya night length ratio is set as Y1 road lamp lighting electricity consumption to Ya road lamp lighting electricity consumption, and the coordinate scatter points corresponding to Y1 night length ratio to Ya night length ratio as the abscissa and Y1 road lamp lighting electricity consumption to Ya road lamp lighting electricity consumption as the ordinate are marked in a plane rectangular coordinate system;

[0150] If the marked plurality of coordinate scatter points are in a linear correlation relationship, the electricity consumption fitting equation is set as y = αx + β;

[0151] The plurality of road lamp lighting electricity consumptions are subjected to average number calculation to obtain a period electricity consumption average value, and the plurality of night length ratios are subjected to average number calculation to obtain a period night length average ratio;

[0152] The plurality of road lamp lighting electricity consumptions and the plurality of period night length average ratios are subjected to covariance calculation to obtain an XY parameter covariance, and the plurality of period night length average ratios are subjected to variance calculation to obtain an X parameter variance;

[0153] The ratio of the XY parameter covariance to the X parameter variance is calculated to obtain a fitting equation undetermined coefficient α, the period electricity consumption average value, the period night length average ratio, and the fitting equation undetermined coefficient α are substituted into the electricity consumption fitting equation, and the fitting equation undetermined coefficient β is calculated to obtain the electricity consumption fitting equation.

[0154] It should be noted here that:

[0155] In the present application, when the period electricity consumption average value and the period night length average ratio are substituted into the electricity consumption fitting equation, the period electricity consumption average value is the y value, and the period night length average ratio is the x value;

[0156] The creation of the electricity consumption fitting equation is shown in the following example:

[0157]

[0158] From the above table, the period night length average ratio is calculated to be 0.5, the period night length average ratio is 120, the XY parameter covariance is Cov(X, Y) = 4 / 3, the X parameter variance is 0.02 / 3, and thus α = 200 and β = 20 are calculated;

[0159] The electricity consumption fitting equation is y = 200x + 20.

[0160] If the marked plurality of coordinate scatter points are not in a linear correlation relationship, a polynomial fitting function is established through Y1 night length ratio to Ya night length ratio and Y1 road lamp lighting electricity consumption to Ya road lamp lighting electricity consumption;

[0161] The polynomial fitting function is specifically as follows:

[0162]

[0163] Wherein, y is the street light lighting power consumption, x is the night length proportion, a0 to a n are coefficients of the polynomial fitting function, and n is the order of the polynomial fitting function.

[0164] The Y1 night length proportion to Ya night length proportion and the Y1 street light lighting power consumption to Ya street light lighting power consumption are substituted into the polynomial fitting function, and the residual sum of squares function RSS of the street light lighting power consumption is calculated.

[0165] The coefficients a0 to a n of the polynomial fitting function in the residual sum of squares function RSS are subjected to partial derivative calculation, and n function expressions containing unknown numbers a0 to a n are obtained, and the n function expressions containing unknown numbers a0 to a n are solved to obtain n equation groups containing a0 to a n , and the equation groups are solved to obtain the specific values of a0 to a n .

[0166] The specific values of a0 to a n are substituted back into the polynomial fitting function to obtain the power consumption fitting equation.

[0167] According to the power consumption fitting equation, the energy storage power of the target photovoltaic street lamp in the street light energy storage period is adjusted.

[0168] Specifically as follows:

[0169] The night length proportion corresponding to the energy storage prediction period is obtained to obtain the prediction period night length proportion, the prediction period night length proportion is substituted into the power consumption fitting equation to obtain the period prediction power consumption.

[0170] The period energy storage amount corresponding to each prediction matching period is obtained, and the period energy storage amount is subjected to average number calculation to obtain the period effective energy storage amount corresponding to the energy storage prediction period. If the period effective energy storage amount is greater than or equal to the period prediction power consumption, the energy storage upper limit value corresponding to the street light energy storage period is set as the energy storage optimization upper limit value. If the period effective energy storage amount is less than the period prediction power consumption, the energy storage upper limit value corresponding to the street light energy storage period is set as the energy storage physical upper limit value.

[0171] It should be noted here that:

[0172] In the present application, the energy storage upper limit referred to here is specifically the maximum energy storage amount corresponding to the target photovoltaic street lamp.

[0173] ​In the present application, the upper limit value of energy storage optimization referred to here is 80% SOC, and the physical upper limit value of energy storage referred to here is specifically 100% SOC.

[0174] It should be noted here that:

[0175] In the present application, the energy storage device 20%~80% SOC can meet the target photovoltaic street lamp to complete the lighting period of the street lamp lighting.

[0176] The battery capacity corresponding to the target photovoltaic street lamp at the current time is obtained, and the real-time battery capacity is obtained. The time interval between the current time and the end time point of the street lamp energy storage period is obtained, and the available charging duration is obtained.

[0177] It should be noted here that:

[0178] The sunset time deviation between each lighting matching period and the real-time energy storage period is obtained, and a sunset time deviation reference value is set. The sunset time average value of the lighting matching period whose sunset time deviation is less than the sunset time deviation reference value is calculated, and the end time point of the street lamp energy storage period is obtained.

[0179] The sunset time deviation reference value referred to here is specifically 5 min.

[0180] The available charging duration, the real-time battery capacity and the energy storage upper limit value are used to calculate the energy storage preset power, and the energy storage power corresponding to the target photovoltaic street lamp is adjusted to the energy storage preset power.

[0181] The energy storage preset power is calculated, and the specific formula is as follows:

[0182] ;

[0183] Wherein, Wys is the energy storage preset power, Cns is the energy storage upper limit value, Srl is the real-time battery capacity, and Tkc is the available charging duration.

[0184] It should be noted here that:

[0185] If the period effective energy storage amount is greater than or equal to the period predicted power consumption, the energy storage upper limit value is the energy storage optimization upper limit value, and if the period effective energy storage amount is less than the period predicted power consumption, the energy storage upper limit value is the energy storage physical upper limit value.

[0186] The preferred embodiments of the application disclosed above are only to facilitate the elucidation of the application. The preferred embodiments do not describe all the details of the application and limit the application to the specific embodiments. Obviously, many modifications and variations can be made in light of the teachings above. The description is chosen and described in order to provide the best illustration of the application and its practical application to those skilled in the art and to enable those skilled in the art to best utilize the application. The application is limited only by the claims and their full scope and equivalents.

Claims

1. A method for self-restoring power supply and energy storage management of photovoltaic streetlights, characterized in that, Includes the following steps: Step S1: Obtain the target photovoltaic street light, set the lighting area corresponding to the target photovoltaic street light as the target lighting area, perform real-time light monitoring on the target lighting area, divide the target lighting area into lighting time periods based on the monitoring results, and obtain lighting time period division data; Step S2: Formulate a self-recovery power supply strategy for the target photovoltaic street light based on the lighting time period division data, obtain the energy storage prediction cycle and real-time energy storage cycle, match the real-time energy storage cycle with the historical time period of strategy execution by light intensity and cloud thickness to obtain real-time cycle matching data, match the energy storage prediction cycle with the historical time period of strategy execution by light intensity and cloud thickness to obtain prediction cycle matching data, and obtain historical cycle matching data. Step S3: By analyzing the predicted cycle matching data, a power consumption fitting equation is created to adjust the energy storage power of the target photovoltaic streetlights during the streetlight energy storage period. Step S3 specifically includes the following steps: Step S31: Based on the real-time periodic matching data, represent the street light power consumption and night length ratio corresponding to each lighting matching cycle using coordinates to obtain multiple coordinate scatter points; Step S32: If the multiple marked coordinate points are linearly correlated, then the night length ratio and street lighting power consumption are linearly fitted; if the multiple marked coordinate points are not linearly correlated, then the night length ratio and street lighting power consumption are polynomially fitted to obtain the power consumption fitting equation. Step S33: Adjust the energy storage power of the target photovoltaic streetlights during the streetlight energy storage period according to the power consumption fitting equation; Step S33 specifically includes the following steps: The night length percentage corresponding to the energy storage prediction cycle is obtained to get the night length percentage of the prediction cycle. The night length percentage of the prediction cycle is substituted into the electricity consumption fitting equation to get the predicted electricity consumption of the cycle. The cycle energy storage corresponding to each prediction matching cycle is obtained, and the average of the obtained cycle energy storage is calculated to obtain the cycle effective energy storage corresponding to the energy storage prediction cycle. If the cycle effective energy storage is greater than or equal to the cycle prediction electricity consumption, the energy storage upper limit value corresponding to the street light energy storage period is set as the energy storage optimization upper limit value. If the cycle effective energy storage is less than the cycle prediction electricity consumption, the energy storage upper limit value corresponding to the street light energy storage period is set as the energy storage physical upper limit value. The battery capacity corresponding to the target photovoltaic street light at the current moment is obtained to obtain the real-time battery capacity. The time interval between the current moment and the end time of the street light energy storage period is obtained to obtain the available charging time. The available charging time, real-time battery capacity, and energy storage upper limit are used to calculate the preset energy storage power, and the energy storage power corresponding to the target photovoltaic street light is adjusted to the preset energy storage power.

2. The method for self-restoring power supply and energy storage management of photovoltaic streetlights according to claim 1, characterized in that, Step S1 specifically includes the following steps: Step S11: Acquire the photovoltaic street light to obtain the target photovoltaic street light, and set the street light illumination area corresponding to the target photovoltaic street light as the target illumination area; Step S12: Set the regional environmental monitoring cycle, conduct environmental monitoring on the target lighting area within the regional environmental monitoring cycle, divide the regional environmental monitoring cycle into street light energy storage period, mixed power supply period and direct power supply period, and obtain lighting period division data.

3. The method for self-restoring power supply and energy storage management of photovoltaic streetlights according to claim 2, characterized in that, Step S12 specifically includes the following steps: Step S121: Real-time monitoring of ambient brightness in the target lighting area to obtain the real-time ambient brightness value, and setting a preset value for lighting brightness. If the real-time ambient brightness value is less than or equal to the preset value for lighting brightness, the acquisition time point corresponding to the real-time ambient brightness value is set as the lighting on time point. If the real-time ambient brightness value is greater than the preset value for lighting brightness, the acquisition time point corresponding to the real-time ambient brightness value is set as the lighting off time point, thus obtaining multiple lighting on time points and multiple lighting off time points. Step S122: The period from the lighting turn-on time to the lighting turn-off time is set as the street light lighting period, and the period from the lighting turn-off time to the lighting turn-on time is set as the street light energy storage period. A sliding time window is set to perform power monitoring and traversal for each street light lighting period. Based on the monitoring results, the sliding time window is divided into a fully lit window and a partially lit window. The period covered by the partially lit window is set as the mixed power supply period, and the period covered by the fully lit window is set as the direct power supply period. Step S122 specifically includes the following steps: The real-time power generation of the photovoltaic power generation panel of the street lamp is obtained within the sliding time window to obtain the real-time power generation of the window. The real-time lighting power of the street lamp corresponding to the sliding time window is obtained to obtain the real-time lighting power of the window. If the real-time power generation of the window is greater than the real-time lighting power of the window, the sliding time window is set as a fully lit window. If the real-time power generation of the window is less than or equal to the real-time lighting power of the window, the sliding time window is set as a partially lit window.

4. The method for self-restoring power supply and energy storage management of photovoltaic streetlights according to claim 1, characterized in that, Step S2 specifically includes the following steps: Step S21: Obtain lighting time period segmentation data, and formulate segmented power supply strategies for target photovoltaic streetlights that are in the streetlight energy storage period, mixed power supply period, and direct power supply period based on the lighting time period segmentation data; Step S22: Obtain the historical working cycles of the target photovoltaic street light using the segmented power supply strategy for self-recovery power supply, and obtain multiple historical working cycles; Step S23: If the current time is within the street light energy storage period, then set the current natural date as the real-time energy storage cycle; if the current time is not within the street light energy storage period, then set the next natural date corresponding to the current natural date as the real-time energy storage cycle, and set the next natural date corresponding to the real-time energy storage cycle as the energy storage prediction cycle. Step S24: Match the real-time energy storage cycle with multiple historical working cycles based on the illumination conditions. Based on the matching results, select multiple lighting matching cycles from the historical working cycles to obtain real-time cycle matching data. Step S25: Match the energy storage prediction cycle with multiple historical working cycles based on the illumination conditions. Based on the matching results, select multiple prediction matching cycles from the historical working cycles to obtain prediction cycle matching data.

5. The method for self-restoring power supply and energy storage management of photovoltaic streetlights according to claim 4, characterized in that, Step S21 specifically includes the following steps: Acquire information on street light energy storage periods, hybrid power supply periods, and direct power supply periods; If the target photovoltaic street light is in the street light energy storage period, no lighting power will be supplied to the photovoltaic street light. If the target photovoltaic street light is in the mixed power supply period, the photovoltaic output power will be used first to supply lighting power to the photovoltaic street light. If the power is insufficient, the energy storage device will supplement it. If the target photovoltaic street light is in the direct power supply period, the energy storage device will be used directly to supply lighting power to the photovoltaic street light.

6. The method for self-restoring power supply and energy storage management of photovoltaic streetlights according to claim 4, characterized in that, Step S24 specifically includes the following steps: Step S241: Randomly select a sample historical cycle within the historical working cycle, match the real-time energy storage cycle with the sample historical cycle to the light intensity time axis, and set a light matching window on the light intensity time axis. Step S242: Set the real-time time segment corresponding to the illumination matching window in the real-time energy storage cycle as the first illumination window segment, set the real-time time segment corresponding to the illumination matching window in the sample historical cycle as the second illumination window segment, acquire the illumination intensity of the segment corresponding to the target photovoltaic street lamp in the first illumination window segment to obtain the first segment illumination intensity, acquire the historical illumination intensity of the target photovoltaic street lamp corresponding to the second illumination window segment to obtain the second segment illumination intensity, calculate the difference between the first segment illumination intensity and the second segment illumination intensity, and calculate the ratio of the absolute value of the difference to the second segment illumination intensity to obtain the illumination intensity deviation corresponding to the illumination matching window; Step S243: Set a preset range for illumination deviation. If the illumination intensity deviation is within the preset range, set the illumination matching window as an effective illumination matching window. If the illumination intensity deviation is not within the preset range, set the illumination matching window as an invalid illumination matching window.

7. A method for self-restoring power supply and energy storage management of photovoltaic streetlights according to claim 6, characterized in that, Step S24 specifically includes the following steps: Step S244: Use the illumination matching window to slide through the illumination intensity time axis, count the duration of the effective illumination matching windows in the illumination intensity time axis, obtain the effective illumination matching duration, acquire the duration of the illumination intensity time axis, obtain the cumulative illumination monitoring duration, calculate the ratio of the effective illumination matching duration to the cumulative illumination monitoring duration, and obtain the illumination matching degree corresponding to the historical period of the sample. Step S245: Obtain the cloud matching window and the cloud thickness time axis. Use the cloud matching window to traverse the cloud thickness time axis to obtain the cloud matching degree corresponding to the historical period of the sample. Step S246: Obtain the cloud matching degree and illumination matching degree corresponding to each historical working cycle; Step S247: Set the cloud reference matching degree and the illumination reference matching degree. Filter the historical working cycles with cloud matching degree greater than or equal to cloud reference matching degree and illumination matching degree greater than or equal to illumination reference matching degree into lighting matching cycles to obtain real-time cycle matching data.

8. The method for self-restoring power supply and energy storage management of photovoltaic streetlights according to claim 1, characterized in that, Step S31 specifically includes the following steps: Multiple lighting matching cycles are obtained based on real-time periodic matching data. The sunrise and sunset times corresponding to each lighting matching cycle are obtained. The difference between the sunset and sunrise times is calculated, and the ratio of the obtained difference to 24 is obtained to get the night length percentage corresponding to each lighting matching cycle. The lighting power consumption of the target photovoltaic street light during the lighting matching cycle is obtained to obtain the lighting power consumption of multiple street lights; The obtained night length percentages are named Y1 night length percentage to Ya night length percentage in ascending order of value. The street lighting power consumption corresponding to Y1 night length percentage to Ya night length percentage is set as Y1 street lighting power consumption to Ya street lighting power consumption. The coordinate points corresponding to Y1 night length percentage to Ya night length percentage as the abscissa and Y1 street lighting power consumption to Ya street lighting power consumption as the ordinate are marked in a plane rectangular coordinate system.

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