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Street Light Distribution Analysis – How to Meet Your Road Lighting Standards!

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This is a requirement for road lamp design.

Item NameRoute CodeRoad Width (m)Surface TypeLamp ConfigurationNumber of LampsLamp Height (m)Lamp Spacing (m)Angle (°)Length of Lamp Arm (m)Distance Between Lamp and Road (m)Illuminance (1m)
Route 1M57mCIE C2 (Calculated Humidity)Unilateral Lamp0.81240000.758000
Route 2M314mCIE C2 (Calculated Humidity)Bilateral Lamp0.81040000.758000

Now, based on the above conditions, we need to select the light distribution for the lamps and verify it.

First, let’s analyze the road conditions.

For Route 1, with a road width of 7m, this should be a two-lane road with unilateral lamp arrangements, pole spacing of 40m, and pole height of 7.5m.

For Route 2, with a road width of 14m, this should be a four-lane, bidirectional road with bilateral lamp arrangements, pole spacing of 40m, and pole height of 9m.

Based on these road conditions, we proceed with the selection of light distribution, referencing IESNA’s categorization of street lamps.

IESNA Street Lamp Classification

↑ IESNA Street Lamp Classification, North American Lighting Manual 10th Edition

For one to two-lane roads, we typically choose Type II street lamps. Type I is suitable for paths and sidewalks, while Type III applies to main highways.

We can refer to the following rules based on road width.

Road Width Light Distribution Guidance

As per the above table, we should select the Type II L distribution. However, considering the distance of 0.75m between the lamp and the road as specified in the road conditions, we will adjust our pole spacing slightly and choose Type II M or S distribution.

Type II Light Distribution Test

Let’s begin testing Route 1 by setting the road conditions in DIALux evo (we avoid DIALux4.13 as it does not support the EN13201:2015 standard necessary for selecting the new standard).

DIALux evo Road Setting

Here, we need to select the surface type as CIE C2 and check the option for calculating wet road surfaces, choosing W1.

The CIE C2 surface corresponds to asphalt, similar to the reflectivity of our traditional R3. Further explanation of the codes is provided below:

CIE C2 Surface Type Codes

With the road conditions set, we can select the light distribution for verification calculations.

We are going to select a Type II S distribution for verification.

Type II S Distribution Configuration

Set the lamp arrangement conditions and configure the lamp luminous flux to the required 5500lm.

Lamp Configuration Settings

Verification Results

Verification Results for Type II S Distribution

The results were not satisfactory; the road brightness uniformity was below the standard requirement of 0.5cd/m². However, both Uo and Uow, as well as Ul, significantly exceeded the standard values.

We can conclude that the distribution might be slightly inadequate, but where exactly is it lacking? We need to analyze the brightness calculation grid.

Brightness Calculation Grid Analysis

By analyzing the calculation grid above, we found the minimum value, which is lower between the two lamp poles. This indicates that the light distribution needs to be strengthened at both ends, so we will directly choose Type II M distribution for our calculations.

Switching to Type II M Distribution

Type II M Distribution Settings

Verification Results

Results for Type II M Distribution

The results are all satisfactory, indicating that this light distribution can meet client requirements under the specified 5500lm luminous flux.

Next, let’s look at Route 2 and set the road conditions: a four-lane, bidirectional road, M4 standard, calculated wet surface.

Route 2 Conditions Setup

The road conditions for Route 2 are essentially the same as Route 1, except it’s a four-lane bidirectional roadway with bilateral lamp arrangements, upgraded by one level.

We will again choose Type II M distribution for arrangement.

Type II M distribution for Route 2

Verification Results

Results for Route 2 Validation

Both sides met the conditions, indicating that this distribution can satisfy client requirements under the specified 6500lm luminous flux.

Through this analysis, it is evident that there are patterns to follow when selecting light distribution for street lighting. Whether choosing existing products or developing new distributions, one can design according to these rules and then identify defects through calculation results, making targeted modifications accordingly.

Solar Street Light Color Rendering Index

Solar Street Light Color Rendering Index (CRI) Application Guide – Manufacturer’s Perspective

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Understanding the Color Rendering Index (CRI) in Solar Street Lights

The Color Rendering Index (CRI) is a crucial parameter for evaluating the color rendering performance of solar street light sources. The higher the CRI, the better the color reproduction, and the visual effect is closer to natural light. This article analyzes the CRI values of different types of light sources and their impact on visual quality.

As a solar street light manufacturer, we understand that CRI directly affects lighting effects and user experience. Below, we provide practical advice from the perspectives of technical principles, scene adaptation, and product selection.

Solar Street Light Color Rendering Index

1. Comparison of Light Source Types and Color Rendering Characteristics

Light Source TypeCRI (Ra)Spectral CharacteristicsAdaptability Assessment (Solar System)
Incandescent Lamp95-100Continuous spectrum, but lacks blue lightBest color rendering but only 15lm/W efficiency, requires 3x battery capacity, now obsolete
Fluorescent Lamp60-85Line spectrum, lacks red lightDifficult to start at low temperatures (-10℃ brightness drops by 40%), not suitable for cold regions
High-Pressure Sodium Lamp20-25Narrow spectrum yellow light, severe color distortion100lm/W+ efficiency, only used in remote low-cost projects
LED Lamp70-98Adjustable full spectrum/segmented spectrumMainstream choice, high CRI models offer 130lm/W+ efficiency, controllable energy consumption

2. Impact of Solar Street Light CRI on Actual Effects

Safety and Functionality

  • Low CRI (Ra<70): Red warning signs ΔE color difference >15 (international requirement ΔE<5), face recognition distance shortened by 30%.
  • High CRI (Ra≥80): Vegetation layering improves by 50%, reduces “spooky feeling” complaints at night.

Economy and Energy Efficiency

  • For every 10-point increase in Ra: Requires an 8% increase in battery capacity (e.g., 50W street light Ra70→Ra80 requires an additional 10Ah battery).
  • Cost balance: High CRI LED premium is about 0.8-1.2 yuan/W, but maintenance cycle extends by 2-3 years.

Commercial Value

  • Ra≥90: Product color saturation increases by 18%, night-time consumer conversion rate increases by 12% (measured data from commercial squares).

Solar Street Light Color Rendering Index

3. Scenario-Based Selection Scheme

Application ScenarioRecommended Ra ValueKey Technical SolutionCost Sensitivity
Suburban Main Road70-753000K warm white light + asymmetric lens, reduces blue light spill★★☆☆☆
Old Residential Area80-85R9 supplementary light chip (deep red restoration) + anti-glare design★★★☆☆
Cultural Tourism Landscape Belt90-95Full spectrum LED + RGBCW intelligent color adjustment, restores ancient building textures★★★★☆
Industrial Park65-70High efficiency low CRI models, emphasizes uniform illumination★☆☆☆☆

Engineering Suggestions:

  • Key area testing: Use X-Rite CA410 spectrophotometer to measure R9 (deep red) and R12 (deep blue) performance.
  • Hybrid solution: Basic module (Ra70) + key supplementary light module (Ra90), balances cost and effect.

4. Technical Optimization and Quality Control Points

Spectral Enhancement Technology

  • Violet-excited LED: Spectral continuity and similarity to sunlight reaches 92%, Ra≥95 and blue light peak reduced by 40%.
  • Dynamic dimming: Automatically switches to low CRI mode (Ra85→70) during low traffic periods, extends battery life by 30%.

Attenuation Control

  • Annual attenuation standard: High-quality products CRI annual decline ≤1.5, low-quality products can reach 5-8 points.
  • Compensation circuit: Built-in current regulation module, offsets color rendering decline caused by LED chip aging.

Optical Design

  • Compound lens: Secondary light distribution reduces invalid scattering, increases effective color rendering light by 15%.

5. User Purchase Suggestions

  1. Certification standards: Request CIE S 025/E:2015 test report, focus on Rf (fidelity) and Rg (gamut index).
  2. Warranty terms: Choose manufacturers that promise “Ra decline ≤3 within 5 years”, prioritize products supporting modular upgrades.
  3. On-site verification: Use standard color cards (e.g., ColorChecker 24 colors) to compare lighting effects before installation.

Case reference: A certain ancient town project used LED with Ra95+R9>60, increasing night-time visitor stay time by 1.2 hours and shop revenue by 18%.

As a manufacturer, we recommend users choose a “sufficient and economical” color rendering solution based on actual needs, avoiding the cost waste brought by blindly pursuing high parameters. For customized solutions, we can provide spectrum simulation and energy consumption calculation services.

Tag: Solar Street Light CRI

LUXMAN SOLAR STREET LIGHT MANUFACTURER

What makes Luxman different?

Luxman Light puts its customers and quality first. The team boasts a wealth of experience with decades of hands-on knowledge in the lighting and new energy space.

As a global leader in photovoltaic lighting, Luxman partners with businesses to customize innovative power and sustainability solutions that are informed by many years of experience at the cutting-edge of photovoltaics.

+86 13246610420
[email protected]

SOLAR STREET LIGHT MANUFACTURER

solar battery energy storage system

Industrial Energy Storage Meets Automated Solar Panel Cleaning Systems

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Driven by the global energy structure transformation and the “dual-carbon” goals, industrial energy storage technology is evolving from a simple energy storage tool to a core node in the smart manufacturing system. The accompanying fully Automated Solar Panel Cleaning Systems, with its intelligent operation and maintenance capabilities, is becoming a key breakthrough in improving the efficiency and extending the lifespan of energy storage equipment. The following analysis explores this from the dimensions of technological innovation and commercial value.

rade-fully-automated-cleaning-system

1. Five Cutting-Edge Application Scenarios for Industrial Energy Storage

1.1 Smart Grid Peak Shaving

In 2024, a Chinese steel group deployed a 200MW/800MWh iron-chromium flow battery energy storage system, which responds to grid load fluctuations in real-time, saving over 120 million yuan in electricity costs annually. The accompanying drone inspection system reduced fault response time from 6 hours to 15 minutes.

1.2 Microgrid Energy Management

A Southeast Asian rubber industrial park adopted a “photovoltaic + sodium-ion battery” microgrid, combined with AI power prediction algorithms, enabling 24-hour continuous production. The fully automated cleaning robot removes dust from photovoltaic panels daily, increasing power generation efficiency by 18%.

1.3 Heavy Industry Energy Saving Transformation

A German automotive factory integrated a supercapacitor energy storage system to recover braking energy in the stamping workshop. Combined with a laser cleaning device that continuously removes the oxide layer on the capacitor surface, the energy conversion efficiency remains stable at over 92%.

1.4 Data Center Emergency Systems

Microsoft’s Azure data center adopted an immersion liquid-cooled energy storage module, paired with pipeline self-cleaning technology, ensuring 99.999% power supply reliability during the 2024 typhoon season, while reducing single-rack maintenance costs by 40%.

1.5 Distributed Energy Systems

Japan’s 7-Eleven convenience store network deployed modular zinc-air energy storage units, which maintain 85% charge-discharge efficiency in humid environments through cloud-controlled nano-coating cleaning technology.

2. Four Core Advantages of the Fully Automated Solar Panel Cleaning Systems

2.1 Efficiency Revolution

  • Ultrasonic dust removal devices can increase lithium battery cooling efficiency by 30%.
  • Wall-climbing robots enable 360° non-destructive cleaning of flow battery pipelines.
  • Machine vision recognition systems accurately locate electrolyte crystallization areas.

2.2 Cost Control

Traditional ModeAutomated Cleaning System
Manual inspection: ¥1200 per sessionSingle cleaning cost: ¥80
Annual downtime loss: ¥860,000Failure rate reduced by 72%

2.3 Safety Upgrade

Millimeter-wave radar monitors dust concentration inside energy storage cabinets in real-time, combined with negative pressure adsorption technology, reducing the risk of thermal runaway to 0.03 incidents per 10,000 hours, far exceeding national standards.

2.4 Intelligent Operation and Maintenance

  • Blockchain technology records each cleaning parameter.
  • Digital twin systems simulate cleaning cycles under different climate conditions.
  • Self-learning algorithms optimize cleaning agent ratios.

3. Technological Synergy Creates Incremental Value

When industrial energy storage meets fully automated cleaning, it is driving three major business model innovations:

  1. Energy Storage as a Service (EaaS): A complete solution lease including cleaning and maintenance.
  2. Carbon Asset Appreciation: The energy efficiency improvements contributed by the cleaning system can be converted into CCER carbon credits.
  3. Equipment Health Bank: A residual value assessment system based on cleaning data.

Recommended Products – Todos Automatic Solar Panel Cleaning Robot

1. Automatic Solar Panel Cleaning system

  • Cleaning times: once a day;
  • Cleaning effect: more than 98%;
  • Cleaning method: dry sweep, No need for water. The water sweeping function needs to be customized.

It is very suitable for large power station maintenance, especially for large power generation in deserts, cities, and high pollution areas.

Fully Automatic Solar Cleaning Robot

2. Remote Control Solar Panel Cleaning Robots

  • Cleaning method: water washing, dry cleaning;
  • Cleaning effect: more than 98%;
  • Operation mode: semi-automatic;

This is the most commonly used style of cleaning company, easy to transport and carry.

Solar Panel Cleaning Robots

 

Solar street light application solutions

Key Formulas for Solar Street Light Design

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This article summarizes essential formulas commonly used in solar street light design, integrating national standards and practical case studies from various papers:

1. Average Road Illuminance Calculation

Formula:
Eavg = (N × Φ × U × K) / A

  • Parameter Description:
    • N: Number of fixtures
    • Φ: Total luminous flux per lamp (lm)
    • U: Utilization factor (0.4-0.6)
    • K: Maintenance factor (0.7-0.8)
    • A: Road area (m2) = Road width × Lamp spacing

Example:
6m wide road, lamp spacing 30m, using 10,000 lm LED, one-sided lighting:
Eavg ≈ (1 × 10,000 × 0.5 × 0.75) / (6 × 30) ≈ 20.8 lx

Solar street light design

2. Solar Panel Power Calculation

Formula:
Ppv = Qday / (Hpeak × ηsys)

  • Parameter Description:
    • Qday = PLED × Twork (Daily energy consumption, Wh)
    • Hpeak: Local annual average peak sunlight hours (check meteorological data, e.g., Beijing 4.5h)
    • ηsys: System efficiency (0.6-0.75, including line losses, controller losses)

Example:
Load power 80W, daily operation 10h, Shanghai Hpeak=3.8h:
Ppv ≈ (80 × 10) / (3.8 × 0.65) ≈ 324 W

3. Battery Capacity Calculation

Formula:
C = (Qday × D) / (DOD × ηbat × Vsys)

  • Parameter Description:
    • D: Number of consecutive cloudy days (usually 3-5 days)
    • DOD: Depth of discharge (0.5 for lead-acid batteries, 0.8 for lithium batteries)
    • ηbat: Charge/discharge efficiency (0.85-0.95)
    • Vsys: System voltage (12V/24V)

Example:
Daily consumption 800Wh, 24V system, 3 days backup, lithium battery:
C ≈ (800 × 3) / (0.8 × 0.9 × 24) ≈ 138.9 Ah → Choose 150Ah battery

4. Solar Panel Installation Angle

Formula:
θ = φ + (5° to 15°)

  • Parameter Description:
    • φ: Local geographical latitude
    • Winter optimization: latitude +10°~15°, summer optimization: latitude -5°

Example:
Nanjing latitude 32°, fixed bracket tilt angle set at 37° (32°+5°) to improve winter power generation.

5. Wind Pressure on Solar Panels

Formula:
F = 0.61 × v2 × A

  • Parameter Description:
    • v: Maximum wind speed (m/s)
    • A: Wind-facing area of the photovoltaic panel (m2)

Example:
Panel area 2m2, design wind speed 30m/s:
F = 0.61 × (30)2 × 2 = 1098 N
Need to verify the wind resistance of the lamp pole and foundation.

6. Component Operating Voltage Correction (Temperature Effect)

Formula:
Vmp = Vmp(STC) × [1 + α × (T – 25)]

  • Parameter Description:
    • α: Temperature coefficient (approximately -0.35%/°C for monocrystalline silicon)
    • T: Actual operating temperature (°C)

Example:
Nominal component voltage 18V, operating temperature 60°:
Vmp ≈ 18 × [1 – 0.0035 × (60-25)] ≈ 15.3 V

7. Voltage Drop Compensation Due to Temperature

Formula:
ΔV = Nseries × α × ΔT × Vmp(STC)

Example:
3 series-connected components, each Vmp=30V, temperature difference 35°:
ΔV ≈ 3 × (-0.0035) × 35 × 30 ≈ -11V
Need to adjust the MPPT voltage range.

8. Solar Panel Capacity Optimization Design

Empirical Formula:
Ppv(opt) = 1.2 × Ppv

  • Consider shadowing, dust loss (efficiency reduction of 10-20%)
  • When paralleling multiple components, increase bypass diodes to reduce hotspot effects.

9. Typical Design Parameter Comparison Table

ParameterReference ValueStandard Basis
Illuminance uniformity U0≥0.4 (main road)CJJ45-2015 Road Lighting Standards
Component tilt angle error≤±3°GB/T 9535 Photovoltaic Module Standards
Battery cycle life≥1500 times (lithium battery)GB/T 22473 Energy Storage Standards
Wind resistance rating≥12 levels (33m/s)GB 50009 Building Load Code

Note: Actual design should be combined with PVsyst simulations and DIALux lighting simulations, and validated through field tests.