Case Studies
Transportation agencies across the United States are using INVEST to evaluate and improve sustainability within their agency and on their projects.
Case studies focus on the general use of INVEST and its implementation and/or scoring practices. Some focus more on process/application, some focus on a few select criteria, some focus on the overall experience of using INVEST. Case Studies are developed by the agency which submits them, with review and input by FHWA.
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Arizona DOT - LED Life-cycle Costing Analysis and Life-cycle Assessment
Arizona
Lead Agency: Arizona Department of Transportation (ADOT)
INVEST Module: Project Development
Download the Arizona DOT Round 3 Final Report (3,338 kb) and Appendices (18,448 kb)
Converting to light emitting diode (LED) technologies for roadway lighting can produce economic and environmental savings through the use phase of the system. However, there are also impacts associated with other phases of the lighting systems light-cycle that create non-trivial costs and environmental impacts. Life-cycle Costing Analysis (LCCA) and Life-cycle Assessment (LCA) are approaches used to examine the net effects of policies, services, and products. The holistic approach provided by these two methodologies can better describe the efficacy of LED roadway lighting in producing economic and environmental savings. Life-cycle costing focuses on upfront, future and avoided monetary costs while considering the time value of money. Life-cycle assessment focuses on the environmental impacts including impacts associated with material extraction, processing and production, use phase and end-of-life. The usefulness of these methodologies is that they provide a holistic accounting of potential impacts and can identify costs/impacts and potential tradeoffs that are not obvious. LCCA and LCA can help practitioners identify and select strategies that offer long-term economic and environmental benefits. LCCA and LCA are distinct in their approaches but can be complimentary. There is clear overlap between the methodologies and required inputs, especially for systems that require significant and near-continuous energy use. Using established LCCA and LCA guidelines and best practices a framework was developed to provide quantitative results to measure life-cycle costs and emissions of roadway lighting systems and evaluate the efficacy of technology transitions in producing long-term savings and environmental benefits.
When developing an LCCA or LCA it is necessary to define a system boundary. System boundaries are partially subjective and are specified across number of dimensions. To be clear, it is possible that quantitative conclusions reached using one system boundary are different than those using second, larger or smaller, system boundary. The system boundary defined here reflects elements within a roadway lighting system that an agency likely has some element of direct control or responsibility. The LCCA system boundary includes capital, maintenance, and electricity costs. The LCA boundary includes manufacturing, use, and maintenance of luminaries and some system components. The manufacturing phase includes raw material extraction, transportation, and production of interim and final products. The use phase covers impacts associated with energy consumption. The maintenance phase covers maintenance processes. While it is expected that upstream costs outside the system boundary are carried through to later phases and accounted for in an LCCA, environmental impacts and energy use beyond the boundary are not accounted for. In addition, civil infrastructure systems carry long term usage and maintenance commitments. As such, the time horizon of analysis, which is also subjective, can impact final quantitative results. Comparative assessments of two or more system alternatives are further complicated if the operational environment is different. In the case of roadway lighting systems, performance differences between technologies could result in different number of fixtures. This may be particularly true when updating older lighting systems. Fewer total fixtures may be needed to meet nighttime visibility standards in systems using newer and better performing technologies.
Estimating the Energy, Greenhouse Gas, and Cost Savings of LED Roadway Lighting Conversion
The Arizona Department of Transportation (ADOT) is engaged in the early stages of converting existing it’s High Pressure Sodium (HPS) roadway lighting system to Light Emitting Diode (LED). The existing roadway lighting system operated and maintained by ADOT features more than 28,000 luminaries with an operating cost of approximately 4 million dollars. The conversion is expected to produce energy, greenhouse gas emission, and cost savings over the lifespan of the LED system due to reductions in electricity demand and maintenance. Project Development 17 (PD-17) of FHWA’s INVEST tool is specifically designed to promote economic and environmental gains through lighting conversion or on-site renewable power sources. While INVEST promotes sustainability qualitatively it does not provide enough information to develop quantitative economic and environmental assessment. This case study presents a combined LCCA and environmental LCA framework to quantify the potential benefits of lighting conversion. In addition to the benefits already mentioned, LEDs may provide other benefits relative to HPS, including light color and improved visibility, but are not easily captured by quantitative methods like LCCA and LCA. This study first examines an ADOT LED pilot project. Insights from the examination of the existing LED system are used in the development of an LED LCCA and LCA framework.
Recker 202 Pilot Conversion
In September 2016, ADOT installed 36 LED lighting fixtures in place of 36 HPS fixtures along one mile of the State Route 202 freeway. In preparation for this conversion, ADOT conducted a preliminary analysis of this conversion to estimate energy usage and cost savings. As specified by PD-17, the input wattage for HPS (438 Watts) and LED (170 Watts) were used to estimate potential savings. The conversion was expected to result in an energy savings of 61% and near equivalent dollar savings. Billing records show that the HPS system used approximately 48 MWh annually. Using INVEST table PD-17.2.A this specific conversion would have been awarded 3 points towards the PD-17 criteria. Ultimately, an analysis conducted after the system was installed and operational found energy and cost savings of 50% and 48% respectively. To better understand the discrepancy between realized savings and predicted savings an additional assessment was developed from raw energy use data.
While INVEST recommends the use of “input wattage” when estimating reductions in energy use, differences between rated and measured power ratings can be significant in roadway lighting luminaries. The sign and magnitude of these differences can vary by type, model and manufacturer. Where possible, measured wattage should be used rather than ratings specified by equipment manufactures. Among the smart features offered with new LED lighting systems are wireless monitoring systems. The General Electric LED system installed in the pilot project included such a system. One of the variables tracked was instantaneous power demand. We found that the pilot LED luminaries performed closely with specification with an average power demand of 171.4 watts across the 36 luminaries.
A key factor in predicting future energy use of a roadway lighting system is an estimate of total operating hours. INVEST PD-17.2 calls for using a baseline condition of 4,380 hours. Annual sunshine duration varies across the United States and it should be expected that operating hours for roadway lighting hours to differ as well. Quantitative assessments of street lighting performance should use known operating standards for individual lighting systems. Based on the average power demand of individual luminaries and the annual power consumption of the LED array ( ≈ 21,000 kWh from September 2016 to September 2017), we estimate the average annual operating hours of this system to be approximately 3,400 hours (9.3 hours/day) (Equation 1) This is significantly lower than the baseline condition recommended by INVEST. There are two critical assumptions that may account for this discrepancy. First, by using the total energy consumption over the year we are assuming all 36 luminaries were 100% operational. If any fixtures were out for an extended period it would have a significant impact on annual energy use (>1.6 kWh per fixture per day). Second, one of the features and advantages of advanced LED streetlighting systems is the ability to dim lights during dawn and dusk periods or reduce overall brightness thus reducing power demand. We were unable to ascertain the extent to which dimming was used over the course of the year. If adaptive dimming was used it could explain why the estimated annual hours is low. INVEST states that “power savings associated with daylight sensors and activity level sensors” should not be included when estimating power savings. However, these technologies can produce non-negligible energy and costs savings and should be considered in quantitative assessments.
In the four years prior to conversion, the previous HPS system used considerably more energy with an annual average of 41,000 kWh. Using a variant of Equation 1 we attempted to derive some information about the operating conditions of the HPS system. We estimated the expected annual operating hours for the HPS system in four previous years based on the rated power of each luminaire and found significant discrepancies between the HPS and LED systems. From the annual metered consumption, we estimate that the average annual operating hours of each luminaire to be 2,590 and as low as 2,360 hours annually. These values are outside the bounds of what is reasonable. We hypothesize two reasons for the discrepancy. First, HPS power demand is significantly lower than their “input wattage” specification. If the new LED system adopted the same operating hours as the previous system (3,400 hrs/year), we estimate that the average power demand of an HPS luminaire to be 333 Watts, a 24% decrease from the input wattage rating. However, all HPS luminaries tested by Jiang et al. (2015) found that HPS power demand tends to be higher rather than lower when compared to rated performance. It then follows that the measured electricity demand of the previous system does not reflect a fully operational system. Given the 30 month expected lifespan of HPS lamps and time required to service them, it’s possible that a number of the HPS lights were not operational during part or all of each year. The operational status of luminaries in the previous system can have a significant impact on realized energy and emission savings.
From the examination of the 202 Recker conversion we find that the assumptions made by INVEST PD-17 are unlikely to accurately estimate annual energy and costs savings of lighting technology conversion. Accurate estimates of energy and cost savings will be sensitive to differences in rated and measured power demand, annual operating hours, advanced adaptive and dimming light controls, and pre-conversion and post-conversion operating conditions. However, the inclusion of PD-17 is lauded for its potential to move transportation agencies towards more sustainable outcomes. The Recker 202 light conversion reduced ADOT’s annual electricity costs for that stretch of road by $2000 dollars and reduced CO2e emissions by 9,400 kg. The reduction in CO2 e is roughly equivalent to taking two vehicles (12,000 miles per year) off the road. Cost savings were directly determined from billing data and the reduction in CO2e was determined using billing energy use data and U.S. EPA’s eGRID2016 emission rate for Arizona.
Key Findings and Recommendations
- Rated and measured power usage for luminaries can vary significantly. It is recommended that measured power be used when estimating the potential benefits of light technology conversions.
- Relative and absolute energy and emission savings will be impacted if technology is used that allows for adaptive controls and relative light dimming. The extent to which these technologies will be used and their impact on energy consumption should be considered.
- Absolute energy and emission savings will be impacted by total operating hours. When developing estimates of annual energy and emission reductions from the improved energy efficiency of lighting technologies, agencies should rely on known operating hours rather than assuming the 4,380 hours recommended by INVEST.
- Assumptions of systems in fully operational states may not be representative of actual conditions. Prolonged luminaire outages in pre-conversion systems could cause actual cost and emission savings to be significantly lower than expected.
Generalizable Model Framework
Though the specifics of roadway lighting systems are likely to vary from one system to another there are principal components that are expected to have significant impact on the results of LCCA and LCA assessments. These components are listed below and are loosely ordered based on their expected relative impact on the results. The general framework and the relationship between these inputs and final outputs is illustrated in the figure below.
Luminary Input Wattage: The use phase associated with roadway lighting is generally expected to drive most costs and environmental impacts. As described in the assessment of the LED pilot project in Arizona, rated input wattage may not accurately reflect actual power use. When and where possible, input wattage used in these assessments should be measured with multimeters. Additionally, the effect of adaptive lighting controls that temporarily decrease power demand should be considered on general power demand.
Number of Luminaries: The total number of luminaries in the system based on type and wattage.
Current and Future Operating Hours: The operational duration of lights will vary due to seasonality as well as the potential effects of weather. Experience should be used to estimate annual operating hours rather than estimates based on sunrise and sunset times.
Carbon Intensity of Current and Future Electricity Mix: The emissions associated with electricity consumption are dependent on the technology (coal, natural gas, nuclear, etc.) which supplies electricity to the grid. Carbon intensity of energy varies regionally in the United States. To effectively assess future emissions, the evolution of grid energy mixes should be considered. Many states and regions have adopted renewable portfolio standards which can be used to help define future energy mix scenarios.
Electricity Pricing: The costs associated with electricity use are both fixed and variable. Fixed charges often include taxes, account and meter fees while variable charges depend on total usage as well as tiered and seasonal pricing structures. Reviewing past utility bills associated with roadway lighting systems that detail electricity use and total costs can provide a possible range of effective electricity rates. As with the carbon intensity of energy mixes, potential changes in the price of electricity should be considered to estimate future costs.
Luminary and Labor Costs: Theses costs should reflect both the cost of the fixtures, other necessary physical components and installation labor costs. Costs associated with replacement parts and maintenance labor are also required.
Luminary Operational Life: The service life of luminary lamps (total operating hours) and other luminary components. Expected service life drives maintenance cycles and associated replacement and labor costs and environmental impacts.
Luminary and System Material Components and Emission Factors: To determine the upstream environmental impacts of roadway lighting systems a material breakdown of system components is required including luminaries and support structures. The bill of materials can be associated with emission factors found in LCA databases or published literature. In some cases, data points that reflect the entire luminary may exist. In others, the cumulative environmental impact can be estimated through the breakdown of its constituent parts.
LED Life-cycle Costing Analysis and Life-cycle Assessment Generalized Framework
