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The Business Case for Smart Street Lighting as the Smart City Network

January 7, 2023

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Marissa Wright

A 10-Year cost-benefit analysis of Smart Lighting-enabled Smart City sensor and device network.

For owners and managers of street and area lighting to make informed decisions about a Smart Lighting or Smart City sensor project, they need high quality, reliable information to support their business cases. Until now, most of this information has either been scattered across a vast number of sources or buried by blog posts filled with unsourced information.

This article is intended to provide a supportable, accurate framework to help answer these questions:  

If we’ve already upgraded to LED lamps, why should our organization care about Smart Street Lighting? What do street lights have to do with our Smart City vision? 

TLDR-RT (Too Long, Didn’t Read – Read This.)

This business case is based on a comprehensive, detailed model developed at Tondo, and as a result, this paper is longer than a typical blog post. This section explains why you should take time to keep reading.

Street lighting is typically “dusk-to-dawn” controlled and provides uniform lighting for all evening hours regardless of demand. Smart Lighting control enables cities to deliver lighting-on-demand, resulting in significant savings.

A Smart City requires a secure wireless network for sensors and devices to operate on. Light poles with Tondo’s Smart Lighting controls create a city-wide wireless network to enable a city’s Smart City vision.

The cost of the Smart Lighting and Smart City network is typically paid for within 5-6 years by the savings from reduced electricity usage from advanced dimming controls, operational and maintenance costs, increased lamp lifecycles, and reduced connectivity costs of sensors and devices:

  • 60%+ reduction in electricity use and related greenhouse gas footprint
  • 30%+ reduction in operations and maintenance costs
  • 60%+ increase in lamp life
  • 80%+ reduction in cost of leveraging a Smart Lighting network for sensor deployment
  • 4-5 year project payback for Smart Lighting
  • 5-6 year project payback for a Smart Lighting-enabled Smart City sensor network
  • Smart Lighting alone can provide over 50% of the benefits of an LED retrofit project
  • Smart Lighting + Smart City network can provide over 200% of the benefits of an LED retrofit project

Additional non-financial benefits of Smart Lighting include:

Tondo’s Smart Lighting system provides a complete and secure Smart City network and IoT (“Internet of Things”) management platform based on upgradeable open standards technologies.

This standards-based approach enables cities to avoid being locked into proprietary vendor technologies and maximizes the useful life of the Smart City network.

LED Street Lights Still Cost Money.

It’s highly likely you’ve already written a business case for your LED retrofit project, and it’s either in-progress or completed. The case for LED retrofit was, in a nutshell, a savings of approximately 40% in energy costs, and 4x lamp lifecycle savings and reduction in lamp replacement truck rolls.

But an LED retrofit doesn’t take your streetlight costs down to zero.

The next step in street light cost and GHG reduction is to tailor lighting according to pedestrian, driver, and cyclist use based on established lighting standards and best-practices.

Today, most streetlights are controlled by “dusk-to-dawn” photocells or astronomical clocks to detect night-time conditions. The power to a light pole is most often controlled by wiring cabinets that turn the pole power on and off. When the sun goes down, a photocell or astronomical clock tells the cabinet to power the pole and the lights come on.

Dusk-to-dawn control wastes more than 60% of streetlight energy by providing light when it is not needed.

Lighting Environments are Dynamic.

Dusk-to-dawn streetlight control is not only wasteful, but it delivers sub-standard lighting when it cannot adapt to its environment, events, or demand:

  • There are different types of streets serving different transportation needs: local streets, laneways, primary collectors, secondary collectors, primary arterials, secondary arterials, expressways, and different types of highways [5]
  • Lighting demand changes based on volumes of vehicle, cyclist, and pedestrian traffic [9]
  • Vehicle, pedestrian, and cyclist traffic changes based on time of day, day of week, and the month of the year [15]
  • Lighting demand can change for special or unexpected events
  • We have changing amounts of daylight and evening throughout the year and up to 5% between the equator and the Earth’s poles
  • Weather, pole position, and pavement surface affects the quality of by as much as 240%[17]
  • Intersections and crosswalks where pedestrians and vehicles interact have specific lighting requirements [5]

Lamp luminance output – even with LEDs – slowly degrade over time, changing the desired lighting levels. As a result, lamps are either replaced earlier than their useful life, manually adjusted by dispatching field service calls, or the lighting environments are designed to be over-illuminated at initial installation to compensate for expected degradation and still ensure a minimum level of luminance over their planned lifecycle. None of these cases are desirable.

Smart Lighting control delivering standards-based lighting on-demand offers significant opportunities for cost-reduction and improved lighting conditions.

Solution: “Smart” Street Lighting.

Smart street lighting can adjust light levels according to:

  • Roadway classifications set by standards and regulations, such as North America’s ANSI/IES RP-8-21 and Europe’s EN 13201 standards
  • Traffic, cyclist, and pedestrian volumes
  • Ambient light levels during daylight, dusk, evening, and dawn
  • Weather conditions
  • Intersections and crosswalks
  • Special or unexpected events
  • Other safety and security considerations

By delivering standards-compliant lighting on-demand, Smart Lighting becomes a significant source of operational, maintenance, electricity, and CO2e savings.

How Can We Dim Street Lights Safely?

The short answer: by applying the established roadway lighting standards using Smart Lighting controls to roadway and pedestrian demand.

The purpose of street lighting is to provide safe and secure environments for drivers, cyclists, and pedestrians, particularly where they interact with each other. When demand drops, roadway lighting standards allow cities to consider lower illumination levels.

Let’s walk through a case using the North American ANSI/IES RP-8-21 standard for roadway lighting and apply it to a city’s real-world data. [1]

This Canadian city has a population of 92,000 people over 20km sq. and has 7,303 street lights with 99.3% of them upgraded to LED lamps.

The chart below shows the city’s share of roadway by classification type:

From the chart at left, we can see that 79% of the roadway in the city is classified as Local or Collector, which we expect is highly correlated to commuting, school hours, lunch hours, and shopping hours.

Next, when we look at the vehicle traffic volumes during Dusk-to-Dawn periods, we can also see from the chart at right that:

  • Most traffic volumes are low traffic periods
  • Demand is different for each day of the week

As a result, lighting standards provide for lower lighting levels where there is less likelihood of pedestrian-vehicle conflict.

At left, we can see an example of the luminance standards and best-practices described in the ANSI/IES RP-8-21 standard, “Lighting Roadway And Parking Facilities” that guide roadway lighting design in North America[5].

In this case, we are looking at the required luminance level for a Local road with an R2 or R3 surface (asphalt commonly used in North America for local roads), with low, medium, and high pedestrian sidewalk traffic.

When we put all this data together, we see that 60% of the city’s roadway lighting can be dimmed 75%-80% of evening hours by as much as 80% compared with peak hour demand.

The only barrier to enabling this reduced electricity, GHG, operating expense, and increased lamp asset life is implementing a Smart Lighting system.

The key to reducing LED street light electricity use, its GHG footprint, extending LED lamp life with dimming is implementing a Smart Lighting system.

Smart Street lighting provides lighting designers, engineers, and a city’s operations team with fine control over the city’s lighting while providing automation and analytics to reduce the time and effort required to manage lighting assets.

Not All Dimming is Equal.

There are several methods of dimming available [6], [7], [8] with Tondo’s Smart Lighting system and  the savings factors from several academic studies were used in our business case:

  • Design-Based – based on dimming-enabled photometric design
  • Statistical – based on historical traffic volumes with all days of week equal
  • Statistical Categorization – based on historical traffic volumes, each day of week is unique
  • Sensor Sampled – sensors used to sample traffic volumes in in 15-minute intervals

Lighting designers and engineers can utilize software applications such as DIALux to design street lighting projects according to North American and European/UK standards. However, Smart Lighting controls enable cities to easily apply and maintain these designs where lamps and luminaires have different lighting characteristics, respond to traffic pattern changes, and adjust output according to lamp luminance that degrades over a lamp lifecycle.

The photometric Design-based dimming is intended to be used together with one of the three  adaptive traffic-based dimming methods: Simple Statistical, Categorized Statistical, or Sensor-Based.

These four types of dimming are compared in the chart below with Dusk-to-Dawn and Always-On operating costs for:

  • Electricity use [2]
  • GHG footprint measured in CO2 equivalents and available carbon credits [3],[4]
  • Lamp lifecycle [10], [16]
  • Maintenance [11], [12], [13]

Let’s zoom in on how the four dimming types differ with the chart at right:

In the charts above and at right, we can see that Smart Lighting controls can enable as much as a 50% reduction in street lighting operating costs and 68% reduction in electricity and CO2 equivalents GHG footprint.

That brings us to the question of, How does Smart Lighting provide the platform for a Smart City sensor network?

Smart Street Lighting = Smart City Network.

There are many definitions of what a Smart City is, and the definitions continue to evolve. However, a fair summary of those Smart City definitions is a city that:

  • Supports operational efficiencies through technology-based automation
  • Supports economic growth through the provision of technology infrastructure
  • Supports citizen satisfaction with high-availability self-service for city services
  • Supports community development through education, sharing resources, and
  • Supports citizen engagement through connection with city representatives and voting
  • Decreases the human impact of growing urbanization on our environment and wildlife
  • Improves safety and security
  • Supports equal access to city resources for all citizens

A Smart City uses technologies and innovation to reduce the impact of urban growth on our environment and improve service efficiencies, citizen engagement, community development, education, and economic growth for the benefit of all citizens and businesses.

So how can a Smart Lighting-enabled Smart City Network help with a sensor deployment strategy?

Sensors and the Smart City.

Smart Lighting directly and materially reduces the human impact of growing urbanization on our environment and wildlife with reduced energy use, GHG footprint, and sky-glow that impacts human health, animal and bird migration and reproduction, and the aesthetic and scientific research value of a dark night-time sky.

Operational efficiencies, economic growth, improved safety and security, and citizen self-service delivery for Smart Cities depends on better information – faster. Sensors help cities gather data to more effectively manage a range of services including:

  • Transportation
  • Flood control
  • Water quality
  • Gas or fluid leak detection
  • Sanitation services
  • Parking occupancy
  • City asset theft or vandalism
  • Air quality
  • Public safety and first-responder resource management
  • Infrastructure health and degradation

Most sensors today require proprietary networks or use costly individual cellular connections.

Tondo’s Smart Lighting creates a secure, city-wide network that not only enables control over lighting, but also enables connectivity for wireless sensors and devices that enable Smart City applications.

Today, most sensors, cameras, and other connected devices used for remote monitoring and control by city operations teams have their own wireless connection by cellular or satellite connectivity, As a result, the cost of deploying sensors to fully support a wide range of Smart City initiatives using multiple proprietary networks or dedicated cellular connections can become cost-prohibitive.

The chart at left compares the cost of deploying three sensors per city block using a proprietary SaaS-based sensor solution with its own network compared with deploying the same sensors purchased at a capital cost and deployed on a Tondo Smart Lighting-enabled network.

By using a Smart Lighting-enabled city-wide sensor network that is already cloud-connected and integrated back into the city’s own operations management systems, the cost of yet-another-sensor-network or a high cellular connectivity cost is avoided.

With Tondo’s open, standards-based network and open, standards-based management platform, the city is not locked in to a specific vendor’s products – including Tondo’s.

Controlling Smart City Sensor Deployment Costs.

Let’s look at a manhole cover use-case as an example. Why manhole covers instead of water quality, gas leak detection, storm drain levels, or many other use-cases? This article in the New York Times caught my eye recently: Where have all the manhole covers gone? .

Manhole sensors cost hundreds or in some cases, thousands of dollars, with monitoring fees charged monthly. When the number of sensors is relatively low, the operational costs of these sensors are not always noticeable, with SaaS or monitoring costs from $10 to $50 or more per sensor per month charged by some vendors.

The city that was used for street and lighting data in this article [1] has 7,303 standard light pole street lights, 2,537 city street blocks, and 269 crosswalk-marked intersections. The city also has 4,060 sewer manholes and 3,444 storm drain manholes for a total of 7,504 manholes – more manholes than street lights.

Using an assumption of $10.00 per month in SaaS costs per sensor for a proprietary or independent sensor network, the adjacent chart shows the cumulative net costs over a 10-year period.

Smart Lighting creates a net cash-flow positive Smart City sensor solution, where an independent sensor strategy spirals quickly out of control.

No Smart Lighting = No Smart City.

So before the Smart City can be built, we need a cost-effective, secure, wireless, city-wide communications platform (“network”) that can support a wide variety of standards-based sensors and devices that will deliver on the promise of Smart City automation and efficiency.

Street lighting is the natural platform for the Smart City network:

  • Street lighting is everywhere there are infrastructure assets, people, and vehicles
  • Street poles are pre-wired for power
  • Streetlight poles are high up in the air for optimal wireless network position
  • Streetlight poles are convenient locations for a wide range of sensors and devices
  • Streetlight poles are co-located to underground city infrastructure
  • Connection standards already exist to connect network controllers to street lights

Tondo’s Smart Lighting creates a secure, open standards-based city-wide network that not only enables control over lighting, but also enables cost-effective connectivity for wireless sensors and devices that support Smart City goals.

This cash-flow positive Tondo’s Smart Lighting solution for 7,303 luminaires and  2,537 city blocks offsets the cost of the Smart City network, reaching a combined break-even for a 3-sensor-per-city-block project in approximately 6 years and avoids significant future costs.

The Bottom-Line.

This is a lot of information to take in. If you’ve read all the way through, congratulations – hopefully this has been helpful.

If you’ve skipped to the end, and you’re familiar with your original business case for LED retrofit, the adjacent chart shows the relative cost savings between LED Retrofit, Smart Lighting, and a Smart Lighting + Smart City sensor network.

The key takeaways from this case are:

  • Established standards allow for dimming of street lights according to vehicle and pedestrian volumes that can reduce street light operating costs by as much as 50%
  • The annual operating benefit of a Smart Lighting-enabled Smart City network can exceed  200% of an LED retrofit project
  • 79% of roadway is well-suited for reduced light levels
  • Over 80% of evening hours are well-suited for reduced light levels
  • Tondo’s Smart Lighting solution by itself has a payback period of approximately 5 years
  • Tondo’s Smart Lighting solutions also support connectivity and management of Smart City sensors
  • A network platform for sensors and devices is necessary to enable a Smart City strategy
  • Light poles and street light luminaires are the only viable platform for Smart City wireless networks
  • A combined Smart Lighting and Sensor project has a payback period of approximately 6 years
  • Tondo Smart Lighting provides a cash-flow positive platform for Smart City lighting, sensors, and other connected IoT devices

If you have questions or comments for us on this article, please contact us through the Contact Us form on our website.

A Smart Lighting project savings by itself can provide over 50% of the net benefits of an LED Retrofit project.

A Smart Lighting-enabled Smart City network for connected sensors and devices can provide over 200% of the net benefits of an LED Retrofit project.

Methodology, Sensitivities, and Assumptions.

This article is based on a comprehensive economic model developed internally at Tondo that uses a city’s ArcGIS data, Google Maps data, and established street lighting standards to accurately assess the costs and benefits of Tondo’s Smart Lighting and Smart City network projects. However, there are a number of assumptions and input values that may cause significant changes to these predicted values.

Sensitivities for this business case include but are not limited to:

  • Changes to ANSI/IES RP-8-21 roadway lighting standards [1]
  • Cost of electricity [2]
  • Composition of lighting assets and corresponding input wattage [1]
  • CO2 footprint of electricity [3]
  • Cost of CO2 carbon credits if available [4]
  • Additional project management, provisioning, and configuration costs
  • Cost of third-party software integration
  • Any custom software or hardware development required in a project
  • Capital cost of sensors [14]
  • Number of luminaires [1]
  • Crosswalk-based intersections [1]
  • Number of streets for each road surface and classification [1]
  • Currency conversion rates between Canadian and U.S. dollars, if applicable
  • Assumptions for sensor ratio to number of city blocks used in the case [1]
  • SaaS cost assumptions for proprietary sensor solutions used in the case [14]
  • Non-standard lighting levels required by city practices
  • Assumption that all existing city luminaires support dimming control and industry-standard ANSI C136.41 or Zhaga Book 18 sockets

Although it is not practical to cover all aspects of our business case model in this article, our internal model does allow for us to tailor these and other assumptions for a specific use-case.

If you are interested in having your city’s data used to produce a similar cost-benefit analysis of your own Smart Lighting and Smart City Network initiative, please contact us through this website and we would be happy to help.

Citations and Sources.

[1] Data used for Streets, Intersections, Manholes, Lamps, and Luminaires: https://opendata.victoria.ca

[2] Cost per kWh for British Columbia Large Commercial Customers, https://www.hydroquebec.com/data/documents-donnees/pdf/comparison-electricity-prices.pdf

[3] Greenhouse Gas / CO2e footprint per kWh, https://www2.gov.bc.ca/assets/gov/environment/climate-change/cng/methodology/2020-pso-methodology.pdf

[4] Greenhouse Gas / CO2e costs, https://www2.gov.bc.ca/gov/content/environment/climate-change/clean-economy/carbon-tax

[5] Standards used to calculate dimming values for Streets, Intersections, Vehicle and Pedestrian Traffic ANSI/IES RP-8-21, https://webstore.ansi.org/standards/iesna/ansiiesrp21

[6]  Enhancing Energy Efficiency of Adaptive Lighting Control, Adam Sedziwy, Leszek Kotulski, and Artur Basiura; N.T. Nguyen et al. (Eds.): ACIIDS 2017

[7] Economic Impact of Intelligent Dynamic Control in Urban Outdoor Lighting, Igor Wojnicki, Sebastian Ernst and Leszek Kotulski, 25 April 2016

[8] Roadway Lighting Retrofit: Environmental and Economic Impact of Greenhouse Gases Footprint Reduction, Sedziwy, A.; Kotulski, L.; Basiura, A.; 29 October 2018

[9] Traffic volume classifications re: ANSI/IES RP-8-21:  District of North Vancouver, https://www.dnv.org/sites/default/files/edocs/road-classification-strategy.pdf

[10] Effect of adaptive control on the LED street luminaire lifetime and on the lifecycle costs of a lighting installation, J Askola MSc., P Karha DSc, H Baumgartner DSc, S Porrasmaa BSc and E Ikonen DSc, 16 March 2021

[11] Smart street lighting system and existing system of East Main Street, El Cajon (A model for the assessment of energy‑efficient smart street lighting—a case study, Shekar Viswanathan, Shamsullah Momand, Mohibullah Fruten, Alejandro Alcantar, 2021)

[12] BC Hydro street light service rates: https://app.bchydro.com/accounts-billing/rates-energy-use/electricity-rates/street-lighting-service-rates.html

[13] Technical and economic analysis of a Smart Public Lighting model, Bucci, F.; Annunziato, M.; Moretti, F.; EPJ Web of Conferences 33, 05010 (2012)

[14] Cost of manhole cover sensors: City of Torrance, CA, https://torrance.granicus.com/MetaViewer.php?view_id=8&clip_id=11817&meta_id=211562

[15] Effects of high traffic flow on dimmable hours, Author, Google Maps 2022

[16] Impact of luminaire models on optical efficiency/lumens per watt: AcuityBrands ATB0 luminaire technical specifications, 2022

[17] The Study of Lighting Quality of LED and HPS Luminaires Based on Various Road Surface Properties, Suntiti Yoomak , and Atthapol Ngaopitakkul, 2018

[18] Maryland Energy Administration, https://energy.maryland.gov/govt/Documents/NEWSLETTER%20Formatted%20Jul%202021.pdf

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Tondo Mobile Field App Dashboard view screenshot

D4i is the DALI standard for intelligent, IoT-ready luminaires.

By taking care of control and power requirements, D4i makes it much easier to mount sensors and communication devices on luminaires. In addition, intelligent D4i LED drivers inside the luminaire have the capability to store and report a wide range of luminaire, energy and diagnostics data in a standardized format.

Infographic of Bluetooth Technology Global Standards

Highly reliable hardware, firmware, and software components that perform specific, critical security functions. Because roots of trust are inherently trusted, they must be secure by design. Roots of trust provide a firm foundation from which to build security and trust.

Read more at the National Institute of Standards and Technology: Roots of Trust

The 0.1, 0.2, and 0.5 accuracy class electricity meters established within ANSI C12.20-2015 are accurate to within +/-0.1%, +/-0.2%, and +/-0.5% of true value at a full load.

Read more at the ANSI Blog: ANSI C12.20-2015 – Electricity Meters – 0.1, 0.2, and 0.5 Accuracy Classes.

Source: US Department of Energy 2015 U.S. Lighting Market Characterization, issued November 2018

The full Excel data set that accompanies this report can be downloaded here.

Tondo uses the ARM Cryptocell 310 cryptographic chip. Read more about the ARM 300 family here: ARM Cryptocell 300 Family Overview

The world would collectively achieve 10,546 TWh of energy savings by 2030 [with energy efficient lighting], a sum comparable to over 40% of the world electricity generation in 2011. Saving this amount of energy would prevent the emissions of 5,400 Mt CO2, a figure equivalent
to over 15% of the global emissions in 2011.

Source: United Nations Environment Programme (2014). Green Paper - Policy Options to Accelerate the Global Transition to Advanced Lighting.