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

Abstract

The invention discloses a self-recovery power supply and energy storage management method for a photovoltaic street lamp, relates to the field of public lighting, and solves the problem that the existing self-recovery power supply and energy storage management method for the photovoltaic street lamp is poor in application effect. S2, making a power supply strategy for a target photovoltaic street lamp according to the illumination time period division data, obtaining an energy storage prediction period and a real-time energy storage period, and carrying out illumination intensity matching and cloud layer thickness matching with a strategy execution historical time period. And S3, by analyzing and predicting the period matching data, creating an electricity consumption fitting equation to carry out energy storage power adjustment on the target photovoltaic street lamp. According to the method, the service life of the photovoltaic street lamp can be prolonged.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of public lighting, relates to photovoltaic power generation technology, and specifically relates 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: 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; 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 that shallow charging and shallow discharging of the energy storage device are difficult to achieve, thereby resulting in a decrease in the cycle life of the energy storage device and affecting the service life of the photovoltaic street lamp.

[0003] Therefore, the application provides a photovoltaic street lamp self-recovery power supply and energy storage management method. SUMMARY

[0004] In view of the defects in 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.

[0005] 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: 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 target lighting area into lighting periods according to the monitoring result, and obtaining lighting period division data; 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; Step S3: creating a power consumption fitting equation by analyzing the prediction period matching data, and adjusting the energy storage power of the target photovoltaic street lamp in the street lamp energy storage period.

[0006] Further, the step S1 specifically includes the following steps: Step S11: The target photovoltaic street lamp is obtained by acquiring the photovoltaic street lamp, and the street lamp lighting area corresponding to the target photovoltaic street lamp is set as the target lighting area; Step S12: The regional environment monitoring period is set, and the environment of the target lighting area in the regional environment monitoring period is monitored, and the regional environment monitoring period is divided into a street lamp energy storage period, a hybrid power supply period and a direct power supply period, to obtain lighting period division data.

[0007] Further, the step S12 specifically includes the following steps: Step S121: The real-time brightness of the target lighting area is monitored to obtain the real-time brightness value, and a lighting brightness preset value is set. If the real-time brightness value is less than or equal to the lighting brightness preset value, the acquisition time point corresponding to the real-time brightness value is set as the lighting opening time point. If the real-time brightness value is greater than the lighting brightness preset value, the acquisition time point corresponding to the real-time brightness value is set as the lighting closing time point, to obtain a plurality of lighting opening time points and a plurality of lighting closing time points; Step S122: The period from the lighting opening time point to the lighting closing time point is set as the street lamp lighting period, and the period from the lighting closing time point to the lighting opening time point is set as the street lamp energy storage period. A sliding time window is set to monitor the power of 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 period covered by the non-complete lighting window is set as the hybrid power supply period, and the period covered by the complete lighting window is set as the direct power supply period; The step S122 specifically includes the following steps: The real-time power generation of the street lamp photovoltaic panel in the sliding time window is obtained to obtain the window real-time power generation, and the street lamp lighting power corresponding to the sliding time window is obtained to obtain the window real-time lighting power. If the window real-time power generation is greater than the window real-time lighting power, the sliding time window is set as the complete lighting window. If the window real-time power generation is less than or equal to the window real-time lighting power, the sliding time window is set as the non-complete lighting window.

[0008] Further, the step S2 specifically includes the following steps: Step S21: The lighting period division data is acquired, and the segmented power supply strategy is formulated according to the lighting period division data for the target photovoltaic street lamp in the street lamp energy storage period, the hybrid power supply period and the direct power supply period; Step S22: The history working period of the target photovoltaic street lamp using the segmented power supply strategy for self-recovery power supply is acquired to obtain a plurality of history working periods; Step S23: If the current time is in the street lamp energy storage period, set the natural date where the current time is as the real-time energy storage period, 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 the real-time energy storage period, and set the next natural date corresponding to the real-time energy storage period as the energy storage prediction period; Step S24: Match the real-time energy storage period with the plurality of historical working periods in terms of light conditions, and select a plurality of lighting matching periods from the historical working periods according to the matching result to obtain real-time period matching data; Step S25: Match the energy storage prediction period with the plurality of historical working periods in terms of light conditions, and select a plurality of prediction matching periods from the historical working periods according to the matching result to obtain prediction period matching data.

[0009] Further, the step S21 specifically includes the following steps: obtaining the street lamp energy storage period, the hybrid power supply period and the direct power supply period; 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 output power is preferentially used for lighting power supply of the photovoltaic street lamp, and the energy storage device is used for supplementing when 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 for lighting power supply of the photovoltaic street lamp.

[0010] Further, the step S24 specifically includes the following steps: Step S241: randomly selecting a sample historical period in the historical working period, matching the real-time energy storage period and the sample historical period to the light intensity time axis, and setting a light matching window on the light intensity time axis; Step S242: setting the real-time time segment corresponding to the light matching window in the real-time energy storage period as a first light window segment, setting the real-time time segment corresponding to the light matching window in the sample historical period as a second light window segment, obtaining the segment light intensity corresponding to the first light window segment of the target photovoltaic street lamp to obtain the first segment light intensity, obtaining the historical light intensity corresponding to the second light window segment of the target photovoltaic street lamp to obtain the second segment light intensity, calculating the difference between the first segment light intensity and the second segment light intensity, and calculating the ratio of the absolute value of the obtained difference to the second segment light intensity to obtain the light intensity deviation corresponding to the light matching window; 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, if the light intensity deviation is not in the light deviation preset interval, setting the light matching window as a light ineffective matching window.

[0011] Further, the step S24 specifically includes the following steps: Step S244: using the light matching window to slide through the light intensity time axis, counting the time length of the light effective matching window existing in the light intensity time axis to obtain the effective light matching time length, obtaining the light monitoring cumulative time length by counting the time length of the light intensity time axis, calculating the ratio of the effective light matching time length and the light monitoring cumulative time length to obtain the light matching degree corresponding to the sample historical period; Step S245: obtaining the cloud layer matching window and the cloud layer thickness time axis, using the cloud layer matching window to traverse the cloud layer thickness time axis to obtain the cloud layer matching degree corresponding to the sample historical period; Step S246: obtaining the cloud layer matching degree and the light matching degree corresponding to each historical working period; Step S247: setting the cloud layer reference matching degree and the light reference matching degree, screening the historical working period with the cloud layer matching degree greater than or equal to the cloud layer reference matching degree and the light matching degree greater than or equal to the light reference matching degree as the lighting matching period to obtain the real-time period matching data.

[0012] Further, the step S3 specifically includes the following steps: Step S31: according to the real-time period matching data, representing the street light lighting power consumption and the night length proportion corresponding to each lighting matching period by coordinates to obtain a plurality of coordinate scatter points; Step S32: if the marked plurality of coordinate scatter points are in a linear correlation relationship, linearly fitting the night length proportion and the street light lighting power consumption, and if the marked plurality of coordinate scatter points are not in a linear correlation relationship, polynomial fitting the night length proportion and the street light lighting power consumption to obtain the power consumption fitting equation; Step S33: according to the power consumption fitting equation, adjusting the energy storage power of the target photovoltaic street lamp in the street light energy storage period.

[0013] Further, the step S31 specifically includes the following steps: According to the real-time period matching data, obtaining a plurality of lighting matching periods, obtaining the sunrise time and the sunset time corresponding to the lighting matching period, calculating the difference between the sunset time and the sunrise time, and obtaining the ratio of the obtained difference and 24 to obtain the night length proportion corresponding to each lighting matching period; Obtaining the lighting power consumption of the target photovoltaic street lamp corresponding to the lighting matching period to obtain a plurality of street light lighting power consumptions; The obtained plurality of night length ratios are sequentially named as Y1 night length ratio to Ya night length ratio in ascending order of values, the Y1 night length ratio to the Ya night length ratio is set as Y1 street lamp lighting power consumption to Ya street lamp lighting power consumption respectively, the Y1 night length ratio to the Ya night length ratio is taken as the abscissa, and the Y1 street lamp lighting power consumption to the Ya street lamp lighting power consumption is taken as the ordinate corresponding to the coordinate scatter point in the plane rectangular coordinate system.

[0014] Further, the step S33 specifically includes the following steps: The night length ratio corresponding to the energy storage prediction period is obtained to obtain a prediction period night length ratio, the prediction period night length ratio is substituted into the power consumption fitting equation to obtain a period prediction power consumption; The period energy storage corresponding to each prediction matching period is obtained, and the obtained period energy storage is subjected to average number calculation 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, 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; 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; The available charging duration, the real-time battery capacity and the energy storage upper limit value 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.

[0015] In summary, due to the adoption of the above technical scheme, the beneficial effects of the present application are: 1. The present application divides the power generation period of the photovoltaic street lamp into the street lamp energy storage period, the hybrid power supply period and the direct power supply period by combining the environmental brightness and the power generation power of the photovoltaic power generation panel, thereby improving the electric energy utilization efficiency of the photovoltaic street lamp.

[0016] 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 charging upper limit value by analyzing the prediction result, so as to realize the shallow charging and 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

[0017] In order to facilitate the understanding of those skilled in the art, the present application will be further described below in conjunction with the drawings.

[0018] Figure 1 The present application is an embodiment step diagram. Figure 2 Schematic diagram of target illumination area of the present application; Figure 3 Schematic diagram of light matching window of the present application; Wherein, 1 is the target illumination area, 2 is the light matching window, and 3 is the light intensity time axis. DETAILED DESCRIPTION

[0019] 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, rather than all. 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.

[0020] Embodiment one 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: Step S1: Real-time light monitoring is performed on the target illumination area, and the target illumination area is divided into an illumination period according to the monitoring result, to obtain illumination period division data; In the step S1, the following steps are specifically included: Please refer to Figure 2 The photovoltaic street lamp that needs to be powered and energy storage managed is obtained to obtain the target photovoltaic street lamp, and the street lamp illumination area corresponding to the target photovoltaic street lamp is set as the target illumination area; It should be noted here that: 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.

[0021] Each natural day is set as a regional environmental monitoring period, and the target illumination 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, to obtain illumination period division data; Specifically as follows: The target illumination area is monitored in real time for environmental brightness to obtain an environmental real-time brightness value, 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, to obtain a plurality of lighting opening time points and a plurality of lighting closing time points; It should be noted here that: In this application, the preset value of the lighting brightness referred to here is the environmental brightness value when the street lamp lighting needs to be turned on.

[0022] The period of extending any one lighting-on time point to the lighting-off time point is set as the street lamp lighting period, the period of extending any one lighting-off time point to the lighting-on time point is set as the street lamp energy storage period, a sliding time window is set to monitor the power of 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 period covered by the non-complete lighting window is set as the hybrid power supply period, and the period covered by the complete lighting window is set as the direct power supply period. It should be noted here that: The street lamp lighting period referred to here includes the hybrid power supply period and the direct power supply period.

[0023] Specifically as follows: In the process of monitoring the power generation of the target photovoltaic street lamp, a sliding time window is set to obtain the real-time power generation of the photovoltaic panel of the street lamp corresponding to the sliding time window, and the window real-time power generation is obtained, the lighting power of the target photovoltaic street lamp corresponding to the sliding time window is obtained, and the window real-time lighting power is obtained, if the window real-time power generation is greater than the window real-time lighting power, the sliding time window is set as a complete lighting window, if the window real-time power generation is less than or equal to the window real-time lighting power, the sliding time window is set as a non-complete lighting window.

[0024] Step S2: According to the lighting 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 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 thickness of the strategy execution history period to obtain prediction period matching data, and the history period matching data is obtained. In the step S2, the following steps are specifically included: The lighting 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 lighting period division data; Specifically as follows: According to the lighting period division data, the street lamp energy storage period, the hybrid power supply period and the direct power supply period are obtained respectively. 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 output power is preferentially used for lighting power supply of the photovoltaic street lamp, and the energy storage device is used for supplementing when 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 for lighting power supply of the photovoltaic street lamp; It should be noted here that: In the present application, the photovoltaic output power referred to here is specifically the real-time generated electric energy directly used for lighting power supply of the target photovoltaic street lamp.

[0025] The historical working periods of the target photovoltaic street lamp using the segmented power supply strategy for self-recovery power supply are acquired, and a plurality of historical working periods are obtained. It should be noted here that: In the present application, the historical working period includes the street lamp energy storage period, the hybrid power supply period and the direct power supply period corresponding to a complete normal day. 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, 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. It should be noted here that: In the present application, since the self-recovery power supply and energy storage in the present application need to be performed in real time, 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, at this time the current time is not in the street lamp energy storage period, since the street lamp energy storage period is already in the past, 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).

[0026] The real-time energy storage period and the plurality of historical working periods are matched for illumination conditions, and a plurality of lighting matching periods are selected from the plurality of historical working periods according to the matching result, and real-time period matching data is obtained. Specifically as follows: Please refer to Figure 3 , a sample historical period is randomly selected from the plurality of historical working periods, the real-time energy storage period and the sample historical period are matched to the illumination intensity time axis, and an illumination matching window is set on the illumination intensity time axis. It should be noted here that: The sample historical period is selected here as a sample, a random selection method is adopted here, a repetitive operation will be performed on the analysis process of the sample historical period, and this is only an example, so no excessive requirements are made on the selection process. In the present application, the time length corresponding to the light intensity time axis here is consistent with the real-time energy storage period and the historical working period, both of which are 24 hours; In the present application, the light matching window referred to here is specifically a time segment that can slide on the light intensity time axis; In the present application, the length of the light matching window referred to here is set to 5 minutes.

[0027] The real-time time segment corresponding to the light matching window in the real-time energy storage period is set as the first light window segment, and the real-time time segment corresponding to the light matching window in the sample historical period is set as the 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; It should be noted here that: In the present application, the segment light intensity referred to here is specifically the average light intensity of this period.

[0028] 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. It should be noted here that: 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. In the present application, the light effective matching window referred to here includes the case where the light intensity deviation is at the boundary of the light deviation preset interval.

[0029] The light intensity time axis is slidingly traversed using the light matching window, the length of the light effective matching window existing in the light intensity time axis is counted to obtain the effective light matching length, the length of the light intensity time axis is obtained to obtain the light monitoring cumulative length, and the ratio of the effective light matching length to the light monitoring cumulative length is calculated to obtain the light matching degree corresponding to the sample historical period. 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; It should be noted here that: In this application, the time length 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; In this application, the cloud matching window referred to here is specifically a time segment that can slide on the cloud thickness time axis; In this application, the duration of the cloud matching window referred to here is set to 5 minutes.

[0030] 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. 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. It should be noted here that: 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, 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. In this application, the cloud effective matching window referred to here includes the case where the cloud thickness deviation is in the boundary of the cloud deviation preset interval.

[0031] Slide the cloud matching window on the cloud thickness time axis, and count the duration of the cloud effective matching window in the cloud thickness time axis to obtain the effective cloud matching duration. Obtain the duration of the cloud thickness time axis to obtain the cloud monitoring cumulative duration. Calculate the ratio of the effective cloud matching duration to the cloud monitoring cumulative duration to obtain the cloud matching degree corresponding to the sample historical period. The cloud matching degree and the light matching degree corresponding to the sample historical period are repeatedly obtained, and the cloud matching degree and the light matching degree corresponding to each historical working period are obtained respectively; It should be noted here that: In the present application, the cloud thickness acquisition device here is a laser ceilometer.

[0032] Here, the cloud matching window is slid over the cloud thickness time axis, the effective cloud matching window existing in the cloud thickness time axis is time length counted, and the effective cloud matching time length is obtained. This process includes two operations: 1. Since the sample historical period and the real-time energy storage period are in time axis alignment, and the first cloud window segment and the second cloud window segment are in synchronization in the time axis, here the cloud thickness of the same period in different periods is matched; 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 window segment is nine o'clock to nine thirty of the first natural date, then the second cloud window segment is nine o'clock to nine thirty of the second natural date; The above matching method can ensure the time period synchronization of the matching process; 2. The cloud effective matching window is time length counted through the classification result of the cloud window segment in 1, the effective cloud matching time length is obtained, the cloud thickness time axis is time length acquired, the cloud monitoring cumulative time length is obtained, the ratio of the effective cloud matching time length to the cloud monitoring cumulative time length is calculated, the cloud matching degree corresponding to the sample historical period is obtained, which can improve the quantitative standard of the cloud matching degree, and the segment matching result is transitioned to the period matching result, providing a basis for the cloud matching between two periods; The subsequent light matching is the same.

[0033] The cloud reference matching degree and the light reference matching degree are set respectively, the historical working period with the cloud matching degree greater than or equal to the cloud reference matching degree and the light matching degree greater than or equal to the light reference matching degree is screened as an illumination matching period, and real-time period matching data is obtained; It should be noted here that: In the present application, the historical illumination matching period screened by the energy storage management system is obtained, the cloud matching degree corresponding to each historical illumination matching period is obtained, the cloud matching degree with the smallest value is set as the cloud reference matching degree, the light matching degree corresponding to each historical illumination matching period is obtained, and the light matching degree with the smallest value is set as the light reference matching degree.

[0034] The acquisition process of real-time period matching data is repeated, the energy storage prediction period is matched with multiple historical working periods under illumination conditions, multiple prediction matching periods are selected from the multiple historical working periods according to the matching result, and prediction period matching data is obtained; It should be noted here that: Here, the energy storage prediction period is matched with multiple historical working periods under the conditions of illumination intensity and cloud thickness, and multiple prediction matching periods are obtained, which are as follows: 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 electric energy it generates. Conversely, as the illumination intensity weakens, 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, allowing the energy storage system to charge quickly. In the morning or evening, the illumination intensity is weak, and the power generation is significantly reduced; Cloud thickness has an important regulating effect on illumination intensity. Thin clouds have a certain scattering effect on light, which weakens the illumination intensity reaching the ground, but the overall distribution is relatively uniform. Thick clouds, on the other hand, strongly block and absorb solar radiation, resulting in a significant reduction in ground illumination intensity, and even the possibility of no light. For example, in cloudy weather, the cloud cover changes constantly, sometimes thin clouds cover the sky, and sometimes thick clouds accumulate. The power generation of photovoltaic panels also fluctuates accordingly. By analyzing the changes in cloud thickness in historical working periods, the stability of photovoltaic power generation under different cloud thicknesses can be understood, providing a basis for predicting the power generation of the current energy storage period.

[0035] The prediction period matching data and the real-time period matching data are defined as historical period matching data; It should be noted here that: The historical period matching data includes the prediction period matching data and the real-time period matching data; The prediction period matching data includes the time period corresponding to each prediction matching period, the sunrise and sunset times, and the period energy storage; The real-time period matching data includes each lighting matching period, and the time period, sunrise and sunset times, and lighting electricity consumption corresponding to each lighting matching period; The prediction matching period and the lighting matching period referred to here are historical time periods.

[0036] It should be noted here that: Step S2 implements self-recovery power supply strategy and multi-dimensional period matching analysis, which can realize the accurate response and intelligent optimization of the photovoltaic street lamp system to dynamic environmental conditions: the differentiated power supply strategy formulated based on the lighting period division data can combine the energy storage prediction period and the real-time energy storage period, dynamically calibrate the energy management parameters through the historical-real-time two-dimensional matching of the light intensity and the cloud thickness, and the real-time period matching data can correct the current power supply mode in real time to ensure that the system quickly adapts to sudden weather changes, and the prediction period matching data can optimize the charging and discharging strategy in the future period in advance through historical law mining to avoid overcharging and overdischarging risks.

[0037] Step S3: creating a power consumption fitting equation based on the analysis of the prediction 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 and the power consumption fitting equation; In the step S3, the following steps are specifically included: Specifically as follows: 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; It should be noted here that: 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.

[0038] The lighting power consumption of the target photovoltaic street lamp in each lighting matching period is obtained to obtain a plurality of street lamp lighting power consumptions; The plurality of night length ratios obtained are sequentially named Y1 night length ratio to Ya night length ratio in order of value from small to large, the street lamp lighting power consumptions corresponding to Y1 night length ratio to Ya night length ratio 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 ratio to Ya night length ratio 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; If the plurality of coordinate scatter points marked are in a linear correlation relationship, the power consumption fitting equation is set as y=αx+β; The plurality of street lamp lighting power consumptions are averaged to obtain a period power consumption average, and the plurality of night length ratios are averaged to obtain a period night length average ratio; The plurality of street lamp lighting power consumptions and the plurality of period night length average ratios are covariance calculated to obtain an XY parameter covariance, and the plurality of period night length average ratios are variance calculated to obtain an X parameter variance; The ratio of the XY parameter covariance and the X parameter variance is calculated to obtain the undetermined coefficient a of the fitting equation. The average of the period electricity consumption, the average proportion of the period night length, and the undetermined coefficient a of the fitting equation are substituted into the electricity consumption fitting equation, and the undetermined coefficient b of the fitting equation is calculated to obtain the electricity consumption fitting equation.

[0039] It should be noted here that: In this application, when the average of the period electricity consumption and the average proportion of the period night length are substituted into the electricity consumption fitting equation, the average of the period electricity consumption is y, and the average proportion of the period night length is x. When creating the electricity consumption fitting equation, an example is as follows: From the above table, the average proportion of the period night length is 0.5, the average proportion of the period night length is 120, the XY parameter covariance is Cov(X, Y)=4 / 3, and the X parameter variance is 0.02 / 3. Thus, a=200 and b=20 are calculated. The electricity consumption fitting equation is y=200x+20.

[0040] If the marked multiple coordinate scatter points do not have a linear correlation relationship, a polynomial fitting function is established through Y1 night length proportion to Ya night length proportion and Y1 street lamp lighting electricity consumption to Ya street lamp lighting electricity consumption. The polynomial fitting function is as follows: ; Wherein, y is the street lamp lighting electricity consumption, x is the night length proportion, a0 to a n are the coefficients of the polynomial fitting function, and n is the order of the polynomial fitting function. Y1 night length proportion to Ya night length proportion and Y1 street lamp lighting electricity consumption to Ya street lamp lighting electricity consumption are substituted into the polynomial fitting function respectively to calculate the residual sum of squares function RSS of the street lamp lighting electricity consumption. The partial derivatives of the coefficients a0 to a n of the polynomial fitting function in the residual sum of squares function RSS are calculated to obtain n function expressions containing unknown numbers a0 to a n , and the n function expressions containing unknown numbers a0 to a n are solved to obtain n sets of equations containing a0 to a n , and the equations are solved to obtain the specific values of a0 to a n . The specific values of a0 to a n are substituted back into the polynomial fitting function to obtain the electricity consumption fitting equation. According to the electricity consumption fitting equation, the energy storage power of the target photovoltaic street lamp in the street lamp energy storage period is adjusted. The specific implementation is as follows: The proportion of night length corresponding to the energy storage prediction period is obtained, and the proportion of night length in the prediction period is obtained. The proportion of night length in the prediction period is substituted into the electricity consumption fitting equation to obtain the period prediction electricity consumption; The period energy storage corresponding to each prediction matching period is obtained, and the average of the obtained period energy storage is calculated to obtain the 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 electricity consumption, the energy storage upper limit value corresponding to the street lamp energy storage period is set as the energy storage optimization upper limit value. If the period effective energy storage is less than the period prediction electricity consumption, the energy storage upper limit value corresponding to the street lamp energy storage period is set as the energy storage physical upper limit value; It should be noted here that: In the present application, the energy storage upper limit referred to here is the maximum energy storage corresponding to the target photovoltaic street lamp; In the present application, the energy storage optimization upper limit value referred to here is 80% SOC, and the energy storage physical upper limit value referred to here is specifically 100% SOC.

[0041] It should be noted here that: In the present application, the energy storage device 20%~80% SOC can meet the target photovoltaic street lamp to complete the lighting of the street lamp lighting period.

[0042] The battery capacity corresponding to the target photovoltaic street lamp at the current time is obtained to obtain the real-time battery capacity. The time interval between the current time and the end time point of the street lamp energy storage period is obtained to obtain the available charging duration; It should be noted here that: 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 to obtain the end time point of the street lamp energy storage period; The sunset time deviation reference value referred to here is specifically 5 min.

[0043] The available charging duration, real-time battery capacity and 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; The energy storage preset power is calculated, and the specific formula is as follows: ; 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.

[0044] It should be noted here that: If the period effective energy storage 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 is less than the period predicted power consumption, the energy storage upper limit value is the energy storage physical upper limit value.

[0045] The preferred embodiments of the application disclosed above are only used to help explain 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 according to the content of the specification. The specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the application, so that those skilled in the art can well understand and use the application. The application is limited only by the claims and their full scope and equivalents.

Claims

1. A photovoltaic street light self-recovery power supply and energy storage management method, 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.

2. A photovoltaic street light self-restoration power supply and energy storage management method 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. A photovoltaic street light self-restoration power supply and energy storage management method 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. A photovoltaic street light self-restoration power supply and energy storage management method 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. A method for self-restoring power supply and energy storage management of a photovoltaic street lamp 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. A photovoltaic street light self-restoration power supply and energy storage management method 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 photovoltaic street light self-restoration power supply and energy storage management method 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 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.

9. A method for self-restoring power supply and energy storage management of photovoltaic streetlights according to claim 8, 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.

10. A method for self-restoring power supply and energy storage management of photovoltaic streetlights according to claim 8, characterized in that, 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.

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