Light adaptive power generation regulation system for photovoltaic curtain wall of elevated subway station

Abstract

The present application relates to photovoltaic power generation regulation technical field, specifically disclose a kind of subway elevated station photovoltaic curtain wall illumination adaptive power generation regulation system, by collecting solar irradiance, azimuth, elevation angle, ambient temperature, train schedule and real-time position, station each power consumption branch power consumption load and photovoltaic module real-time voltage current data, generate dynamic shadow blocking timing, station power consumption load timing curve and maximum power generation power timing curve, according to supply-demand power deviation generates inclination adjustment sequence and energy storage charge-discharge sequence under the constraint of energy storage charge-discharge limit value and grid fluctuation limit value, solve the problem that fixed inclination is difficult to adapt to train dynamic shadow and illumination angle change, the problem that the power generation prediction accuracy is insufficient due to shadow virtualization effect and parasitic resistance nonlinear change, and the problem that power generation output and station power consumption load peak valley misposition, improve the prediction accuracy of photovoltaic curtain wall irradiance and power generation capacity and clean energy on-site consumption level.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power generation control technology, and in particular to an adaptive power generation control system for photovoltaic curtain walls in subway elevated stations. Background Technology

[0002] As a crucial component of urban rail transit systems, elevated subway stations typically feature large-area photovoltaic (PV) curtain walls on their platforms and concourses to provide green energy. However, due to the dense surrounding buildings and the dynamic shadows cast by high-frequency train traffic, existing PV curtain walls, generally installed at fixed angles, struggle to adapt to continuous changes in sunlight angle and shading conditions. This results in a significant discrepancy between the actual irradiance on the module surface and its rated operating conditions, severely limiting the solar energy conversion efficiency. Furthermore, subway operations are characterized by peak passenger flows in the mornings and evenings, causing periodic fluctuations in station electricity load with train arrivals and departures. Existing PV power generation systems often employ constant-voltage grid connection or simple energy storage, failing to establish a linkage mechanism between solar resource prediction and dynamic changes in station load. This leads to frequent misalignments between peak and off-peak power generation and peak and off-peak electricity demand, resulting in insufficient clean energy utilization efficiency and grid connection impact risks. Therefore, a PV power generation control technology that can adapt to the complex sunlight environment of elevated subway stations and coordinate with the station's electricity load is urgently needed. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides a photovoltaic curtain wall solar adaptive power generation control system for elevated subway stations. The technical solution of this system is as follows:

[0004] The multi-source data acquisition module is used to collect solar irradiance data, solar azimuth angle data, solar altitude angle data, ambient temperature data, train timetable data, real-time train location data, power load data of each power branch of the station, and real-time output voltage data and real-time output current data of multiple photovoltaic modules that make up the photovoltaic curtain wall.

[0005] The dynamic shading timing prediction module is used to generate dynamic shading timing information for each of the multiple photovoltaic modules that constitute the photovoltaic curtain wall within a preset time period, based on train timetable data, real-time train location data, solar azimuth angle data, and solar altitude angle data.

[0006] The station load time sequence prediction module is used to generate the station power load time sequence curve of the metro elevated station within a preset time period based on the train arrival and departure time information in the train timetable data, the power load data of each power branch of the station, and the historical power load data of each power branch of the station.

[0007] The photovoltaic power generation capacity time series prediction module is used to generate the maximum power generation time series curve of multiple photovoltaic modules constituting the photovoltaic curtain wall under the current tilt angle state within a preset time period based on solar irradiance data, ambient temperature data, solar azimuth angle data, solar altitude angle data, dynamic shading time series information, real-time output voltage data and real-time output current data of multiple photovoltaic modules constituting the photovoltaic curtain wall.

[0008] The source-load coordinated control module is used to generate a sequence of target values ​​for tilt angle adjustment of multiple photovoltaic modules constituting the photovoltaic curtain wall and a sequence of target values ​​for charging and discharging power of the energy storage device within a preset time period, based on the power supply and demand deviation between the station's power load time-series curve and the maximum power generation time-series curve, while meeting the constraints of the charging and discharging power limits of the energy storage device and the power fluctuation limits of the grid connection point.

[0009] The tilt angle execution module is used to control the tilt angle adjustment mechanism connected to the multiple photovoltaic modules constituting the photovoltaic curtain wall to adjust the multiple photovoltaic modules constituting the photovoltaic curtain wall to their respective target tilt angles according to the tilt angle adjustment target value sequence.

[0010] The energy storage grid-connected control module is used to control the energy storage device to perform corresponding charging and discharging actions and adjust the grid-connected power output of the grid-connected inverter to the station distribution network according to the target charging and discharging power value sequence.

[0011] The technical solution of this invention collects data on solar irradiance, azimuth angle, elevation angle, ambient temperature, train timetable and real-time location, power load of each power branch in the station, and real-time voltage and current of photovoltaic modules. It then generates dynamic shading timing, station power load timing curves, and maximum power generation timing curves. Based on the power supply-demand deviation, and under the constraints of energy storage charging / discharging limits and grid connection fluctuation limits, it generates tilt angle adjustment sequences and energy storage charging / discharging sequences. This solves the problems of fixed tilt angles being unable to adapt to dynamic train shading and changes in illumination angles, existing technologies neglecting the shading blurring effect caused by train speed and the nonlinear changes in parasitic resistance caused by shading leading to insufficient power generation prediction accuracy, and the misalignment between power generation output and station power load peaks and valleys. This improves the accuracy of predicting the irradiance and power generation capacity of photovoltaic curtain walls and the level of local consumption of clean energy.

[0012] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0014] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0015] Figure 1 This is a schematic diagram of an embodiment of a photovoltaic curtain wall illumination adaptive power generation control system for subway elevated stations according to the present invention. Detailed Implementation

[0016] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0017] Figure 1 This diagram illustrates a structural schematic of an embodiment of a photovoltaic curtain wall solar adaptive power generation control system for elevated subway stations provided by the present invention. Figure 1 As shown, the photovoltaic curtain wall solar adaptive power generation control system of the elevated subway station includes:

[0018] The multi-source data acquisition module 110 is used to collect solar irradiance data, solar azimuth angle data, solar altitude angle data, ambient temperature data, train timetable data, real-time train location data, power load data of each power branch of the station, and real-time output voltage data and real-time output current data of multiple photovoltaic modules constituting the photovoltaic curtain wall at the location of the elevated subway station.

[0019] Among them, elevated subway stations refer to urban rail transit stations built above ground and supported by elevated bridge structures; for example, Station A of Metro Line 2 in a certain city is a typical elevated subway station, with the platform level located on a reinforced concrete viaduct 12m above the ground, and the top of the concourse level covered with a photovoltaic curtain wall composed of multiple photovoltaic modules.

[0020] Among them, solar irradiance data refers to the measured value characterizing the radiant power of solar radiation reaching a unit area on the Earth's surface; for example, at 9:00 AM on January 1, 2026, the solar irradiance data at the location of Station A is a direct irradiance of 650 W / m². 2 and diffuse irradiance 150W / m 2The solar azimuth angle data refers to the angle between the projection of sunlight onto the ground plane and the due south direction; for example, at 9:00 AM on January 1, 2026, the solar azimuth angle at the location of Station A is 105° east of south. The solar altitude angle data refers to the angle between sunlight and the ground plane; for example, at 9:00 AM on January 1, 2026, the solar altitude angle at the location of Station A is 42°. The ambient temperature data refers to the measured atmospheric temperature in Celsius around the elevated subway station; for example, at 9:00 AM on January 1, 2026, the ambient temperature at Station A is 28℃.

[0021] The train timetable data refers to the operational plan information recording train numbers, planned arrival times, and planned departure times. For example, the train timetable data for Station A records that train B102 is scheduled to arrive at the station at 9:02 AM on January 1, 2026, and depart at 9:05 AM. Real-time train location data refers to the real-time spatial coordinates of the train body on the track. For example, at 9:01:30 AM on January 1, 2026, the real-time train location data for Station A shows that the front of train B102 is located at a track coordinate point 180m from the station platform centerline in the direction of entry. Power load data refers to the active power consumed by each power branch of the station at the sampling time. For example, at 9:00 AM on January 1, 2026, the power load data for Station A shows that the escalator load branch is 8kW and the air conditioning / ventilation load branch is 35kW.

[0022] In this context, a photovoltaic (PV) curtain wall refers to a facade or roof structure assembled from multiple PV modules using a structural frame, serving both as building envelope and a PV power generation function. For example, the PV curtain wall covering the top of Station A's concourse consists of 120 PV modules, each with a power output of 400W, arranged in a matrix of 10 rows and 12 columns. A PV module is a single PV power generation unit that converts solar energy into direct current (DC) electricity. For example, the PV module located in the 3rd row and 5th column of Station A's PV curtain wall has a rated maximum output power of 400W under standard testing conditions. Real-time output voltage data refers to the instantaneous voltage measurement between the positive and negative terminals of the PV module's DC output. For example, at 9:00 AM on January 1, 2026, the real-time output voltage data of the PV module in the 3rd row and 5th column of Station A's PV curtain wall was 36.5V. Real-time output current data refers to the instantaneous current measurement value flowing through the DC output terminal of the photovoltaic module; for example, at 9:00 AM on January 1, 2026, the real-time output current data of the photovoltaic module in the 3rd row and 5th column of the photovoltaic curtain wall at Station A is 5.2A.

[0023] The dynamic shading timing prediction module 120 is used to generate dynamic shading timing information for each of the multiple photovoltaic modules constituting the photovoltaic curtain wall within a preset time period, based on train timetable data, real-time train location data, solar azimuth angle data, and solar altitude angle data.

[0024] The preset time period refers to the fixed time window length for shading prediction, load prediction, and power generation prediction; for example, the preset time period is set to a 30-minute time interval from 9:00 AM to 9:30 AM on January 1, 2026. Dynamic shading timing information refers to the serialized information describing the change in the shading status of each photovoltaic module over time due to train body shading within the preset time period; for example, during the period when train B102 enters station A from 9:02 AM to 9:05 AM, the dynamic shading timing information of the photovoltaic module in the 3rd row, 5th column of the photovoltaic curtain wall from 9:03:15 AM to 9:03:45 AM displays the shading level indicator as "full shading level indicator".

[0025] The station load time sequence prediction module 130 is used to generate the station power load time sequence curve of the metro elevated station within a preset time period based on the train arrival and departure time information in the train timetable data, the power load data of each power branch of the station, and the historical power load data of each power branch of the station.

[0026] Train arrival and departure time information refers to the records of actual arrival and departure times of trains at the station in the train timetable data. For example, the train arrival and departure time information for Station A on the morning of January 1, 2026 shows that the actual arrival time of train B102 is 9:02:10 AM, and the actual departure time is 9:05:05 AM. The station power load time series curve refers to a continuous curve describing the trend of the station's total power load over a preset period. For example, the station power load time series curve for Station A from 9:00 AM to 9:30 AM on January 1, 2026 shows that the load jumps from 45kW to 78kW when the train arrives at 9:02 AM, and drops back to 50kW after the train departs at 9:05 AM.

[0027] The photovoltaic power generation capacity time-series prediction module 140 is used to generate the maximum power generation time-series curve of multiple photovoltaic modules constituting the photovoltaic curtain wall under the current tilt angle state within a preset time period, based on solar irradiance data, ambient temperature data, solar azimuth angle data, solar altitude angle data, dynamic shading time-series information, real-time output voltage data and real-time output current data of multiple photovoltaic modules constituting the photovoltaic curtain wall.

[0028] The current tilt angle refers to the actual angle between the normal direction of the photovoltaic module surface and the horizontal plane at the moment before adjustment; for example, at 9:00 AM on January 1, 2026, the current tilt angle of the photovoltaic module in the 3rd row and 5th column of the photovoltaic curtain wall at Station A is 30° with the horizontal plane. The maximum power generation time series curve refers to the curve describing the trend of the maximum DC power output of the photovoltaic module under the current tilt angle condition constrained by actual light and temperature conditions over a preset period; for example, the maximum power generation time series curve of the photovoltaic curtain wall at Station A from 9:00 AM to 9:30 AM on January 1, 2026 shows that at 9:03 AM, the power dropped sharply from 32kW to 8kW due to train obstruction, and the power recovered to 33kW after the train left at 9:05 AM.

[0029] The source-load coordinated control module 150 is used to generate a sequence of target values ​​for tilt adjustment of multiple photovoltaic modules constituting the photovoltaic curtain wall and a sequence of target values ​​for charging and discharging power of the energy storage device within a preset time period, based on the power supply and demand deviation between the station's power load time-series curve and the maximum power generation time-series curve, while meeting the constraints of the charging and discharging power limits of the energy storage device and the power fluctuation limits of the grid connection point.

[0030] The power supply-demand deviation refers to the difference between the power value of the station's power load time series curve and the maximum generating power time series curve at the same moment. For example, at 9:04 AM on January 1, 2026, the power load of Station A was 76kW, the maximum generating power was 8kW, and the power supply-demand deviation was 68kW.

[0031] The charging and discharging power limits for energy storage devices refer to the technical constraints on the maximum allowable charging and discharging power of the energy storage devices. For example, the charging and discharging power limits for the energy storage devices at Station A are: charging power not exceeding 100kW and discharging power not exceeding 100kW. The grid connection point power fluctuation limits refer to the upper limit of the allowable power change rate at the connection point between the photovoltaic power generation system and the station's power distribution network. For example, the grid connection point power fluctuation limit for Station A is: power change not exceeding 20kW per minute.

[0032] The tilt angle adjustment target value sequence refers to the ordered set of photovoltaic modules that need to be adjusted to achieve the target tilt angle value at each control moment within a preset time period. For example, the tilt angle adjustment target value sequence for the photovoltaic module in the 3rd row and 5th column at Station A from 9:00 AM to 9:30 AM on January 1, 2026 is: maintain 30° from 9:00 AM to 9:02 AM, adjust to 45° from 9:02 AM to 9:05 AM to compensate for the loss of direct sunlight caused by train shading, and return to 30° after 9:05 AM. The charging and discharging power target value sequence refers to the ordered set of charging or discharging power values ​​that the energy storage device needs to execute at each control moment within a preset time period. For example, the charging and discharging power target value sequence for the energy storage device at Station A from 9:00 AM to 9:30 AM on January 1, 2026 is: discharging power of 60kW from 9:02 AM to 9:05 AM to fill the power generation gap, and charging power of 40kW from 9:05 AM to 9:15 AM to absorb excess photovoltaic power.

[0033] The tilt angle execution module 160 is used to control the tilt angle adjustment mechanism connected to the multiple photovoltaic modules constituting the photovoltaic curtain wall to adjust the multiple photovoltaic modules constituting the photovoltaic curtain wall to their respective target tilt angles according to the tilt angle adjustment target value sequence.

[0034] The tilt adjustment mechanism refers to an electromechanical actuator that is mechanically connected to the photovoltaic module and drives the photovoltaic module to change its tilt angle around a horizontal axis. For example, each photovoltaic module in the A-site photovoltaic wall has a rotating bracket driven by a servo motor installed on its back as a tilt adjustment mechanism. The target tilt angle refers to the predetermined angle value to which the tilt adjustment mechanism adjusts the photovoltaic module at a specific time according to the tilt adjustment target value sequence. For example, at 9:03 AM on January 1, 2026, the target tilt angle of the photovoltaic module in the 3rd row and 5th column of the A-site photovoltaic wall is 45°.

[0035] The energy storage grid-connected control module 170 is used to control the energy storage device to perform corresponding charging and discharging actions and adjust the grid-connected power value output by the grid-connected inverter to the station distribution network according to the charging and discharging power target value sequence.

[0036] The charging and discharging actions refer to the operational behavior of the energy storage device in absorbing or releasing electrical energy from the DC bus according to the target charging and discharging power sequence. For example, at 9:03 AM on January 1, 2026, the energy storage device at Station A performs a charging and discharging operation, discharging 60kW of power to the station's power distribution network. A grid-connected inverter is a power electronic conversion device that converts the DC power generated by the photovoltaic modules and the DC power output from the energy storage device into AC power of the same frequency and phase as the station's power distribution network. For example, Station A has a 150kW grid-connected inverter installed to convert the DC bus power from the photovoltaic curtain wall into 380V AC power. The station's power distribution network refers to the low-voltage AC power supply network formed by the internal power distribution lines and electrical equipment of the elevated subway station. For example, Station A's power distribution network is a 380V three-phase four-wire AC system, supplying power to the station's lighting, escalators, air conditioning, and ventilation loads. The grid-connected power value refers to the AC active power output of the grid-connected inverter to the station's power distribution network; for example, at 9:10 AM on January 1, 2026, the grid-connected inverter of Station A outputs a grid-connected power value of 25kW to the station's power distribution network.

[0037] The technical solution of this embodiment collects data on solar irradiance, azimuth angle, elevation angle, ambient temperature, train timetable and real-time location, power load of each power branch of the station, and real-time voltage and current of photovoltaic modules. It generates dynamic shading time sequence, station power load time sequence curve, and maximum power generation time sequence curve. Based on the power supply and demand deviation, under the constraints of energy storage charging and discharging limits and grid connection fluctuation limits, it generates tilt angle adjustment sequence and energy storage charging and discharging sequence. This solves the problems that fixed tilt angle is difficult to adapt to dynamic shading of trains and changes in illumination angle, the problems that existing technologies ignore the shadow blurring effect caused by train speed and the nonlinear changes in parasitic resistance caused by shading, resulting in insufficient power generation prediction accuracy, and the problem of peak and valley misalignment between power generation output and station power load. It improves the prediction accuracy of photovoltaic curtain wall irradiance and power generation capacity, as well as the level of local consumption of clean energy.

[0038] In one alternative embodiment, the multi-source data acquisition module 110 is specifically used for:

[0039] Solar irradiance data is collected by a solar irradiance sensor installed on the top beam of the photovoltaic curtain wall; solar azimuth and solar altitude angle data are collected by a solar position tracker installed on the side of the photovoltaic curtain wall; ambient temperature data is collected by temperature sensors installed on the back panels of multiple photovoltaic modules constituting the photovoltaic curtain wall; train timetable data and real-time train location data are obtained by connecting to the automatic train monitoring subsystem of the subway signaling system through a communication interface; power load data of each power branch of the station is collected by smart meters installed in the outgoing circuit of the low-voltage distribution cabinet; and real-time output voltage and real-time output current data are collected by voltage and current detection units installed at the DC output terminals of the multiple photovoltaic modules constituting the photovoltaic curtain wall.

[0040] Among them, the solar irradiance sensor refers to an optical measuring instrument installed on the top of the photovoltaic curtain wall for real-time measurement of solar irradiance data; for example, the solar irradiance sensor installed on the top beam of the photovoltaic curtain wall at Station A includes a direct irradiance measurement probe and a diffuse irradiance measurement probe. The solar position tracker refers to an optical positioning device installed on the side of the photovoltaic curtain wall for automatically tracking the sun's position and outputting solar azimuth and solar altitude angle data; for example, the solar position tracker installed on the side of the photovoltaic curtain wall at Station A is model XYZ-21G. The temperature sensor refers to a temperature-sensing element attached to the back panel of the photovoltaic module for measuring the temperature of the photovoltaic module's back panel; for example, the Pt100 platinum resistance temperature sensor installed at the back panel of the photovoltaic module in the 3rd row and 5th column of the photovoltaic curtain wall at Station A outputs an equivalent value of the ambient temperature data. The communication interface refers to the hardware interface and communication protocol stack that enables data exchange between the multi-source data acquisition module 110 and the subway signaling system; for example, the multi-source data acquisition module 110 at Station A connects to the subway signaling system via an RS485 communication interface using the Modbus protocol. The metro signaling system refers to the comprehensive electromechanical system used for train operation control, dispatching and command, and safety protection on metro lines. For example, the metro line 2, to which Station A belongs, uses a wireless communication-based automatic train control system as its metro signaling system.

[0041] The Automatic Train Monitoring (ATM) subsystem refers to the subsystem within the metro signaling system responsible for monitoring train operation status, managing timetables, and automatically adjusting them. For example, the A-station A AM subsystem tracks the location of train B102 in real time and predicts its arrival time. Smart meters are energy metering devices installed in the outgoing circuits of the station's low-voltage distribution cabinets to measure the load data of each power branch and possessing digital communication capabilities. For example, the smart meter installed in the outgoing circuit of the escalator load branch in A-station A is model DTSD341-MC3. Voltage and current detection units are electronic sampling circuits integrated into the photovoltaic module junction box or DC output terminal to measure real-time output voltage and current data. For example, the voltage and current detection unit connected to the DC output terminal of the photovoltaic module in the 3rd row and 5th column of the photovoltaic wall in A-station A uses a Hall current sensor and a resistor divider sampling circuit.

[0042] In the above-mentioned optional methods, a solar irradiance sensor is further deployed on the top beam of the photovoltaic curtain wall, a solar position tracker is deployed on the side, a temperature sensor is deployed on the back panel of the module, a voltage and current detection unit is deployed at the DC output end, the system is connected to the train automatic monitoring subsystem via a communication interface, and a smart meter is deployed in the outgoing circuit of the low-voltage distribution cabinet to achieve synchronous acquisition of multi-source data.

[0043] In one alternative embodiment, the solar irradiance sensor includes a direct irradiance measurement probe and a diffuse irradiance measurement probe. The direct irradiance measurement probe is used to measure direct solar irradiance data, and the diffuse irradiance measurement probe is used to measure diffuse solar irradiance data. The solar irradiance data is synthesized from the direct solar irradiance data and the diffuse solar irradiance data.

[0044] The direct irradiance measurement probe refers to a measuring component in a solar irradiance sensor that receives only direct radiation from the solar disk and blocks scattered radiation; for example, the direct irradiance measurement probe of the solar irradiance sensor at Station A has a field of view of 5° and is equipped with an automatic shading tube. The scattered irradiance measurement probe refers to a measuring component in a solar irradiance sensor that receives only scattered radiation from the sky hemisphere by blocking direct solar radiation with a shading ball; for example, the scattered irradiance measurement probe of the solar irradiance sensor at Station A is equipped with a shading ball with a diameter of 50mm.

[0045] Among the above-mentioned optional methods, a structure for separating the direct and diffuse irradiance measurement probes is further adopted. The direct probe captures direct solar irradiance data, and the diffuse probe captures diffuse solar irradiance data. The two types of data are then combined into solar irradiance data, thereby improving the measurement coverage capability of irradiance in scenarios where direct and diffuse components are separated.

[0046] In one alternative approach, the dynamic shadow timing prediction module 120 is specifically used for:

[0047] The three-dimensional spatial coordinate sequence of the train body on the track is determined based on the real-time position data of the train. The length, width and height parameters of the train body are determined based on the train timetable data. The direction vector of sunlight is constructed based on the solar azimuth angle and solar altitude angle data. By projecting the three-dimensional spatial coordinate sequence of the train body along the direction vector of sunlight onto the plane where multiple photovoltaic modules constituting the photovoltaic curtain wall are located, the shading geometric coverage rate of each photovoltaic module constituting the photovoltaic curtain wall at each moment within a preset time period is obtained. The dynamic shadow shading time sequence information is generated by comparing the shading geometric coverage rate with the preset shading classification threshold.

[0048] Among them, the three-dimensional spatial coordinate sequence refers to the set of sequences of coordinate values ​​of each feature point on the surface of the train body changing over time in the rectangular coordinate system of the track space; for example, the three-dimensional spatial coordinate sequence of the foremost point of the B102 train from 9:01 am to 9:06 am on January 1, 2026 shows that the Y-direction coordinate changes from 180m upon entering the station to 150m upon leaving the station.

[0049] In the above-mentioned optional methods, the three-dimensional coordinate sequence of the vehicle body is further determined according to the real-time position of the train, the length, width and height parameters of the vehicle body are determined according to the train timetable, the light direction vector is constructed by combining the solar azimuth angle and altitude angle, the vehicle body coordinates are projected onto the photovoltaic curtain wall plane along the light direction, the shading coverage of each component is calculated, and dynamic shadow shading time sequence information within a preset time period is generated.

[0050] In one alternative approach, the dynamic shading timing information includes the shading level identifier of each photovoltaic module constituting the photovoltaic curtain wall at each moment within a preset time period. The shading level identifier is determined by comparing the shading geometric coverage with a preset shading classification threshold, and is either an unshaded level identifier, a partial shading level identifier, or a full shading level identifier.

[0051] Among them, the shading level label refers to a discrete level label that describes the severity of shading by comparing the geometric coverage of shading with a preset shading level threshold. For example, the shading level label of the photovoltaic module in the 3rd row and 5th column of the photovoltaic curtain wall on Station A at 9:03:30 am on January 1, 2026 is "full shading level label".

[0052] In the above-mentioned optional methods, the occlusion coverage rate is further compared with the preset occlusion classification threshold to determine the level of no occlusion, partial occlusion or full occlusion, and dynamic shadow time sequence information containing the occlusion degree level of each component at each time is generated to provide a classification judgment benchmark for the quantitative assignment of the occlusion degree coefficient.

[0053] In one alternative embodiment, the station load timing prediction module 130 is specifically used for:

[0054] Based on the train arrival and departure time information in the train timetable data, the start time of the peak arrival period and the start time of the off-peak departure period within the preset time period are extracted. Based on the first historical load waveform characteristics corresponding to the start time of the peak arrival period and the second historical load waveform characteristics corresponding to the start time of the off-peak departure period in the historical power load data of each power branch of the station, and combined with the current sampling time load value in the power load data of each power branch of the station, the time series curve of the station power load is generated by the time series extrapolation algorithm.

[0055] The first historical load waveform characteristic refers to the load change pattern of the historical electricity load curve corresponding to the start time of the peak train arrival period before and after the train's arrival. For example, in the past 30 days, the electricity load waveforms of Station A, 5 minutes before and after the start time of the peak train arrival period, all showed a first historical load waveform characteristic of a load increase of 25kW to 35kW within 3 minutes. The second historical load waveform characteristic refers to the load change pattern of the historical electricity load curve corresponding to the start time of the off-peak train departure period before and after the train's departure. For example, in the past 30 days, the electricity load waveforms of Station A, 5 minutes before and after the start time of the off-peak train departure period, all showed a second historical load waveform characteristic of a load decrease of 20kW to 30kW within 2 minutes.

[0056] In the above-mentioned optional methods, the starting time of the peak arrival time and the off-peak departure time are further extracted based on the train arrival and departure time. The first historical load waveform characteristics and the second historical load waveform characteristics at the corresponding time are retrieved. Combined with the load value at the current sampling time, the time sequence extrapolation is performed to generate the station power load time sequence curve within the preset time period.

[0057] In one alternative approach, the timing extrapolation algorithm employs a load superposition model that considers the triggering jumps at train arrival and departure times. The load superposition model decomposes each power supply branch of the station into basic lighting load branches, escalator load branches, air conditioning and ventilation load branches, signaling equipment load branches, and platform door load branches. Based on the train arrival and departure time information, the load jump amplitudes of the escalator load branches, air conditioning and ventilation load branches, and platform door load branches are determined respectively in the time period before train arrival and after train departure.

[0058] Among them, the load superposition model refers to a load prediction mathematical model that decomposes the total power load of the station into multiple branch loads, predicts the load jump of each branch according to the train arrival and departure time information, and then superimposes and sums them; for example, the load timing prediction module 130 of station A uses the load superposition model to decompose the total load into basic lighting load branch, escalator load branch, air conditioning and ventilation load branch, signaling equipment load branch and platform door load branch.

[0059] The basic lighting load branch refers to the power supply circuit for the constantly lit lighting fixtures that meet the basic illuminance requirements within the station. For example, the LED basic lighting fixtures for the platform and concourse of Station A are powered by the basic lighting load branch, with a load that remains relatively constant at 12kW. The escalator load branch refers to the power supply circuit for the drive motors of the escalators connecting the concourse and platform levels within the station. For example, the four escalators in Station A are powered by the escalator load branch, and the high-frequency operation of the escalators during train arrivals causes the load to rise to 18kW. The air conditioning and ventilation load branch refers to the power supply circuit for the air conditioning chillers, fan coil units, and ventilation fans within the station. For example, during train arrivals, the air conditioning and ventilation load branch in Station A experiences a surge in compressor load to 45kW due to the intrusion of heat and humidity from the opening of platform doors. The signaling equipment load branch refers to the power supply circuit for the signal room equipment and trackside signaling equipment within the station. For example, the signaling equipment load branch in Station A provides uninterruptible power to the station equipment and axle counter of the automatic train monitoring subsystem, with a load that remains stable at 5kW. The platform door load branch refers to the power supply circuit of the drive motor and control unit of the platform edge screen door at the station. For example, in station A, the platform door load branch causes the load to rise to 8kW for a short time when the screen door opening and closing motor operates when the train arrives.

[0060] Among the above-mentioned optional methods, a load superposition model that considers the triggering jump at the train arrival and departure time is further adopted. The station power supply is decomposed into lighting, escalator, air conditioning and ventilation, signaling and platform door branches. The load jump amplitude of escalators, air conditioning and ventilation and platform doors before and after the train arrival and departure time is determined according to the train arrival and departure time.

[0061] In one alternative approach, the source-load coordinated control module 150 is specifically used for:

[0062] An objective function is constructed with the weighted sum of squares of the power deviation sequence between the station's power load time series curve and the maximum generateable power time series curve as the optimization objective. Under the conditions of satisfying the power limit constraints of the energy storage device and the power fluctuation limit constraints of the grid connection point, the minimum value of the objective function is solved by iterative optimization. The tilt angle adjustment target value sequence corresponding to the minimum value of the objective function is output to the tilt angle execution module 160, and the power charge and discharge target value sequence corresponding to the minimum value of the objective function is output to the energy storage grid connection control module 170.

[0063] The objective function refers to a mathematical optimization model that uses the weighted sum of squares of the power supply and demand deviation sequences between the station's power load time-series curve and the maximum generating power time-series curve as its expression. For example, the objective function of the source-load coordinated control module 150 at station A is the sum of the squares of the power supply and demand deviations at each sampling point within a preset time period multiplied by the corresponding time weight coefficient. The weight coefficient is 1.5 during peak train arrival times and 1.0 during off-peak times.

[0064] In the above-mentioned optional methods, an objective function is further constructed with the weighted sum of squares of the power deviation sequence as the optimization objective. Under the constraints of energy storage charging and discharging limits and grid connection fluctuation limits, the minimum value is iteratively optimized to output the tilt angle adjustment and charging and discharging power target value sequence, which respectively drive the tilt angle execution and energy storage grid connection control module 170 to operate.

[0065] In one alternative approach, the photovoltaic power generation capacity time-series prediction module 140 is specifically used for:

[0066] Based on the dynamic shading timing information, the shading degree coefficient of each photovoltaic module constituting the photovoltaic curtain wall is determined at each moment within a preset time period. Based on the solar direct irradiance data, solar diffuse irradiance data, solar azimuth angle data, solar altitude angle data, and shading degree coefficient in the solar irradiance data, the equivalent effective irradiance is calculated, taking into account the non-uniform attenuation of direct irradiance caused by the dynamic shading of the train body and the compensation for multiple reflections on the surface of the train body.

[0067] The formula for calculating equivalent effective irradiance is expressed as follows:

[0068]

[0069] In the formula, Indicates the first component of the photovoltaic curtain wall Each photovoltaic module at time The equivalent effective irradiance, Indicates time Data on direct solar irradiance, Indicates time The direction of direct sunlight and the first The angle between the normal directions on the surface of each photovoltaic module Indicates the first Each photovoltaic module at time The degree of occlusion coefficient and , This indicates the absorption and attenuation factor of direct sunlight on the train's surface. This represents the moderating factor for the blurring effect of shadow edges caused by the train's speed. Indicates time The instantaneous speed of the train body as it passes through the photovoltaic curtain wall area. Indicates time Solar diffuse irradiance data, It represents the reflection enhancement coefficient of sunlight scattered by the surface of the train body.

[0070] It should be noted that the formula for calculating equivalent effective irradiance couples two physical processes—the attenuation of direct irradiance caused by dynamic shading of the train body and the compensation for scattered irradiance from multiple reflections on the train surface—into a unified irradiance model. The direct irradiance component is geometrically corrected based on the cosine of the angle between the sunlight and the normal to the module, and then multiplied by a shading attenuation term that considers the blurring effect of the shadow edge caused by the train's speed. The scattered irradiance component is supplemented by a compensation increment generated by the enhanced reflection from the train surface on top of the basic scattered irradiance, thus comprehensively describing the equivalent irradiance energy actually received by the photovoltaic module under dynamic shading conditions. The above formula serves as the core irradiance calculation basis for the photovoltaic power generation capacity time-series prediction module 140, converting dynamic shading time-series information, direct and scattered irradiance data, solar angle data, and train motion state parameters into equivalent effective irradiance values ​​for each photovoltaic module at various times within a preset time period, providing accurate irradiance input for the subsequent generation of the maximum power generation time-series curve. Among them, the absorption and shading attenuation factor α of the train body surface to direct sunlight, the adjustment factor γ of the train body speed to the blurring effect of the shadow edge, and the reflection enhancement coefficient δ of the train body surface to scattered sunlight can all be obtained by pre-deploying irradiance reference sensors at the subway elevated station and collecting irradiance change data when the train passes, and then calibrating offline using the least squares fitting method. The calibration process is well known to those skilled in the art.

[0071] In the above-mentioned optional methods, the occlusion degree coefficient of each component at each time is further determined based on the dynamic shadow occlusion timing information. Combined with the direct and scattered irradiance, azimuth angle, elevation angle and occlusion degree coefficient, the equivalent received irradiance of the component is calculated, taking into account the non-uniform attenuation of direct irradiance caused by the train's dynamic occlusion and the compensation for multiple reflections on the vehicle surface.

[0072] In one alternative approach, the photovoltaic power generation capacity time-series prediction module 140 is specifically used for:

[0073] Based on the equivalent effective irradiance and ambient temperature data, and combined with the single-diode equivalent circuit model parameters of the multiple photovoltaic modules constituting the photovoltaic curtain wall, the maximum power point output voltage and maximum power point output current of each photovoltaic module are obtained by solving the maximum power point transcendental equation that considers the series resistance loss and parallel resistance leakage current loss inside the photovoltaic module. The maximum power generation time series curve is then generated based on the product of the maximum power point output voltage and maximum power point output current.

[0074] The transcendental equation is expressed as:

[0075]

[0076] In the formula, Indicates the first Each photovoltaic module at time Maximum power point output current, Indicates the first Each photovoltaic module at time Maximum power point output voltage, Indicates the first Each photovoltaic module at time Photocurrent and equivalent effective irradiance They are in direct proportion. Indicates the first Each photovoltaic module at time Backplate temperature The reverse saturation current under, This represents the series resistance value of a photovoltaic module under standard unshaded conditions. This represents the influence coefficient of the increase in series resistance when a photovoltaic module is partially shaded. Indicates the first Each photovoltaic module at time The degree of occlusion coefficient, The diode ideality factor of a photovoltaic module. Indicates at time No. Backsheet temperature of individual photovoltaic modules Thermal voltage under, This represents the parallel resistance value of the photovoltaic module under standard unshaded conditions. This represents the leakage current amplification factor, indicating the decrease in parallel resistance when a photovoltaic module is partially shaded.

[0077] It should be noted that the above transcendental equation is based on the equivalent circuit model of a single diode and introduces correction factors for the degree of shading in the series resistance and parallel resistance terms, respectively. The series resistance increases with the degree of shading to characterize the effect of extended lateral transport paths of charge carriers caused by local shading, while the parallel resistance decreases with the degree of shading to characterize the effect of increased leakage current channels caused by local shading. The photogenerated current is directly proportional to the equivalent effective irradiance to reflect the dominant role of irradiance in the photogenerated charge carrier generation rate. The reverse saturation current is exponentially dependent on the module backsheet temperature to reflect the influence of temperature on the semiconductor junction characteristics. Thus, a transcendental equation describing the electrical output characteristics of photovoltaic modules under partial shading conditions is established. The above transcendental equation is solved numerically to obtain the maximum power point output voltage and maximum power point output current of each photovoltaic module at any time. Then, the maximum power output value at that time is determined by multiplying the two values. The sum of the maximum power output values ​​at all times within a preset time period constitutes the maximum power output time-series curve, providing the source-load coordinated control module 150 with time-series prediction results of photovoltaic power generation capacity.

[0078] In the above-mentioned optional methods, the maximum power point transcendental equation considering series resistance loss and parallel resistance leakage current loss is solved by combining the equivalent received irradiance and ambient temperature with the parameters of the single diode equivalent circuit model. The maximum power point voltage and current values ​​are obtained, and the maximum power generation time series curve is generated based on the product of the two.

[0079] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A photovoltaic curtain wall illumination adaptive power generation control system for elevated subway stations, characterized in that, The system includes: The multi-source data acquisition module is used to collect solar irradiance data, solar azimuth angle data, solar altitude angle data, ambient temperature data, train timetable data, real-time train location data, power load data of each power branch of the station, and real-time output voltage data and real-time output current data of multiple photovoltaic modules that make up the photovoltaic curtain wall. The dynamic shading timing prediction module is used to generate dynamic shading timing information for each of the multiple photovoltaic modules that constitute the photovoltaic curtain wall within a preset time period, based on train timetable data, real-time train location data, solar azimuth angle data, and solar altitude angle data. The station load time sequence prediction module is used to generate the station power load time sequence curve of the metro elevated station within a preset time period based on the train arrival and departure time information in the train timetable data, the power load data of each power branch of the station, and the historical power load data of each power branch of the station. The photovoltaic power generation capacity time series prediction module is used to generate the maximum power generation time series curve of multiple photovoltaic modules constituting the photovoltaic curtain wall under the current tilt angle state within a preset time period based on solar irradiance data, ambient temperature data, solar azimuth angle data, solar altitude angle data, dynamic shading time series information, real-time output voltage data and real-time output current data of multiple photovoltaic modules constituting the photovoltaic curtain wall. The source-load coordinated control module is used to generate a sequence of target values ​​for tilt angle adjustment of multiple photovoltaic modules constituting the photovoltaic curtain wall and a sequence of target values ​​for charging and discharging power of the energy storage device within a preset time period, based on the power supply and demand deviation between the station's power load time-series curve and the maximum power generation time-series curve, while meeting the constraints of the charging and discharging power limits of the energy storage device and the power fluctuation limits of the grid connection point. The tilt angle execution module is used to control the tilt angle adjustment mechanism connected to the multiple photovoltaic modules constituting the photovoltaic curtain wall to adjust the multiple photovoltaic modules constituting the photovoltaic curtain wall to their respective target tilt angles according to the tilt angle adjustment target value sequence. The energy storage grid-connected control module is used to control the energy storage device to perform corresponding charging and discharging actions and adjust the grid-connected power output of the grid-connected inverter to the station distribution network according to the target charging and discharging power value sequence.

2. The adaptive power generation control system for photovoltaic curtain walls in subway elevated stations according to claim 1, characterized in that, The multi-source data acquisition module is specifically used for: Solar irradiance data is collected by a solar irradiance sensor installed on the top beam of the photovoltaic curtain wall; solar azimuth and solar altitude angle data are collected by a solar position tracker installed on the side of the photovoltaic curtain wall; ambient temperature data is collected by temperature sensors installed on the back panels of multiple photovoltaic modules constituting the photovoltaic curtain wall; train timetable data and real-time train location data are obtained by connecting to the automatic train monitoring subsystem of the subway signaling system through a communication interface; power load data of each power branch of the station is collected by smart meters installed in the outgoing circuit of the low-voltage distribution cabinet; and real-time output voltage and real-time output current data are collected by voltage and current detection units installed at the DC output terminals of the multiple photovoltaic modules constituting the photovoltaic curtain wall.

3. The adaptive power generation control system for photovoltaic curtain walls in elevated subway stations according to claim 2, characterized in that, The solar irradiance sensor includes a direct irradiance measurement probe and a diffuse irradiance measurement probe. The direct irradiance measurement probe is used to measure direct solar irradiance data, and the diffuse irradiance measurement probe is used to measure diffuse solar irradiance data. The solar irradiance data is synthesized from the direct solar irradiance data and the diffuse solar irradiance data.

4. The adaptive power generation control system for photovoltaic curtain walls in elevated subway stations according to claim 1, characterized in that, The dynamic shadow timing prediction module is specifically used for: The three-dimensional spatial coordinate sequence of the train body on the track is determined based on the real-time position data of the train. The length, width and height parameters of the train body are determined based on the train timetable data. The direction vector of sunlight is constructed based on the solar azimuth angle and solar altitude angle data. By projecting the three-dimensional spatial coordinate sequence of the train body along the direction vector of sunlight onto the plane where multiple photovoltaic modules constituting the photovoltaic curtain wall are located, the shading geometric coverage rate of each photovoltaic module constituting the photovoltaic curtain wall at each moment within a preset time period is obtained. The dynamic shadow shading time sequence information is generated by comparing the shading geometric coverage rate with the preset shading classification threshold.

5. The adaptive power generation control system for photovoltaic curtain walls in elevated subway stations according to claim 4, characterized in that, The dynamic shading timing information includes the shading level indicator of each photovoltaic module constituting the photovoltaic curtain wall at each moment within a preset time period. The shading level indicator is determined by comparing the shading geometric coverage rate with the preset shading classification threshold, and can be a no-shading level indicator, a partial shading level indicator, or a full-shading level indicator.

6. The adaptive power generation control system for photovoltaic curtain walls in elevated subway stations according to claim 1, characterized in that, The station load timing prediction module is specifically used for: Based on the train arrival and departure time information in the train timetable data, the start time of the peak arrival period and the start time of the off-peak departure period within the preset time period are extracted. Based on the first historical load waveform characteristics corresponding to the start time of the peak arrival period and the second historical load waveform characteristics corresponding to the start time of the off-peak departure period in the historical power load data of each power branch of the station, and combined with the current sampling time load value in the power load data of each power branch of the station, the time series curve of the station power load is generated by the time series extrapolation algorithm.

7. The adaptive power generation control system for photovoltaic curtain walls in elevated subway stations according to claim 6, characterized in that, The timing extrapolation algorithm adopts a load superposition model that considers the triggering jump at the train arrival and departure time. The load superposition model decomposes each power supply branch of the station into basic lighting load branch, escalator load branch, air conditioning and ventilation load branch, signaling equipment load branch and platform door load branch. Based on the train arrival and departure time information, the load jump amplitude of the escalator load branch, air conditioning and ventilation load branch and platform door load branch is determined respectively in the period before the train arrives and after the train departs.

8. The adaptive power generation control system for photovoltaic curtain walls in elevated subway stations according to claim 1, characterized in that, The source-load coordinated regulation module is specifically used for: An objective function is constructed with the weighted sum of squares of the power deviation sequence between the station's power load time series curve and the maximum generateable power time series curve as the optimization objective. Under the conditions of satisfying the power limit constraints of the energy storage device and the power fluctuation limit constraints of the grid connection point, the minimum value of the objective function is solved by iterative optimization. The tilt angle adjustment target value sequence corresponding to the minimum value of the objective function is output to the tilt angle execution module, and the power discharge target value sequence corresponding to the minimum value of the objective function is output to the energy storage grid connection control module.

9. The adaptive power generation control system for photovoltaic curtain walls in elevated subway stations according to claim 1, characterized in that, The photovoltaic power generation capacity time series prediction module is specifically used for: Based on the dynamic shading timing information, the shading degree coefficient of each photovoltaic module constituting the photovoltaic curtain wall is determined at each moment within the preset time period. Based on the solar direct irradiance data, solar diffuse irradiance data, solar azimuth angle data, solar altitude angle data and shading degree coefficient in the solar irradiance data, the equivalent effective irradiance is calculated, taking into account the non-uniform attenuation of direct irradiance caused by the dynamic shading of the train body and the compensation for multiple reflections on the surface of the train body. The formula for calculating equivalent effective irradiance is expressed as follows: ； In the formula, Indicates the first component of the photovoltaic curtain wall Each photovoltaic module at time The equivalent effective irradiance, Indicates time Data on direct solar irradiance, Indicates time The direction of direct sunlight and the first The angle between the normal directions on the surface of each photovoltaic module. Indicates the first Each photovoltaic module at time The degree of occlusion coefficient and , This indicates the absorption and attenuation factor of direct sunlight on the train's surface. This represents the moderating factor for the blurring effect of shadow edges caused by the train's speed. Indicates time The instantaneous speed of the train body as it passes through the photovoltaic curtain wall area. Indicates time Solar diffuse irradiance data, It represents the reflection enhancement coefficient of sunlight scattered by the surface of the train body.

10. The adaptive power generation control system for photovoltaic curtain walls in elevated subway stations according to claim 9, characterized in that, The photovoltaic power generation capacity time series prediction module is specifically used for: Based on the equivalent effective irradiance and ambient temperature data, and combined with the single diode equivalent circuit model parameters of multiple photovoltaic modules constituting the photovoltaic curtain wall, the maximum power point output voltage and maximum power point output current of each photovoltaic module are obtained by solving the maximum power point transcendental equation that considers the series resistance loss and parallel resistance leakage current loss inside the photovoltaic module. The maximum power generation time series curve is generated based on the product of the maximum power point output voltage and maximum power point output current. The transcendental equation is expressed as: ； In the formula, Indicates the first Each photovoltaic module at time Maximum power point output current, Indicates the first Each photovoltaic module at time Maximum power point output voltage, Indicates the first Each photovoltaic module at time Photocurrent and equivalent effective irradiance They are directly proportional. Indicates the first Each photovoltaic module at time Backplate temperature The reverse saturation current under, This represents the series resistance value of a photovoltaic module under standard unshaded conditions. This represents the influence coefficient of the increase in series resistance when a photovoltaic module is partially shaded. Indicates the first Each photovoltaic module at time The degree of occlusion coefficient, The diode ideality factor of a photovoltaic module. Indicates at time No. Backsheet temperature of individual photovoltaic modules Thermal voltage under, This represents the parallel resistance value of the photovoltaic module under standard unshaded conditions. This represents the leakage current amplification factor, indicating the decrease in parallel resistance when a photovoltaic module is partially shaded.

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