Retrofitting a roadway lighting is a process continuously developed in urban environments due to installations aging and technical upgrades. The common example is replacing high pressure sodium fixtures with LED, plasma or induction ones (the last two also belong to gas discharge sources) [1
] and involving various hardware modules (e.g., occupancy sensors) enabling lighting control [2
]. The main problems being analyzed in the related research were reducing energy consumption, improving illumination quality [3
], optimizing investment and maintenance costs [4
It is worth noting that financial optimization of roadway lighting solutions applies also to such non-trivial cases as road tunnel illumination [5
To support retrofit related optimization, a range of computing approaches are proposed including graph-based modeling of lighting systems [7
] or evolutionary algorithms [8
While the computational, technological and financial aspects of lighting design/retrofit are commonly discussed in the literature, a detailed analysis of an environmental impact of lighting system modernization is rather rarely present in the domain research [7
To go beyond the obvious fact that a reduced power usage leads to a decreased GHG footprint, we made both qualitative and quantitative analysis of how using advanced lighting solutions influences a greenhouse gases emission. Moreover, we analyzed economic benefits resulting from a lowered CO emission, showing that the retrofit investment costs can be effectively reduced by up to 10%.
Similarly, as for the roadway lighting optimization improving energy efficiency, we distinguished three possible retrofit scopes.
HID to LED replacement: The only action is installing LED sources in places of HID ones. An upgraded installation is not additionally adjusted (e.g., by fixture dimming).
Optimization of an installation: The scope of this process may vary depending on a particular case, from appropriate lamp dimming and changing the fixture mounting angles or arm lengths to displacement of selected poles. Note that, due to the certain limitations of HID sources, an HID-based installation should be migrated to LEDs prior to luminous flux adjustments.
Introducing control systems: This most advanced approach to upgrading a lighting installation performance can be made at several levels. In the simplest case, it relies on scheduling, where luminous fluxes change according to the predefined rules at fixed hours [11
] and do not depend on actual environment conditions such as traffic flow intensity, ambient light level or weather conditions. In the advanced scenarios, a lighting installation performance is adjusted dynamically on the basis of environment state’s changes reported by a telemetry layer (induction loops, weather and occupancy sensors, etc.).
and other GHG emissions are linearly dependent on the energy usage, we made a parallel analysis of both. This analysis was based on the real-life retrofit, the details of which are presented in Section 3
In this work, we propose the way of linking a quantitative analysis of financial aspects of a lighting installation retrofit with considerations on an environmental influence (in terms of CO
emission) of such a modernization. Thus, an environmental influence can be included quantitatively in a retrofit strategy planning. The goal of this work was threefold: (i) finding an impact of the above three retrofit approaches on the resultant CO
emissions, in terms of their volumes; (ii) finding which lighting classes [13
] contribute the most, in the context of emission reduction, to the final financial gains; and (iii) how the reduced CO
emissions change the annual costs of a lighting installation performance.
This article is structured as follows. In Section 2
, we overview the approaches listed above and present estimations of expected energy savings for each of them. Section 3
contains the case study of the retrofit carried out in Cracow, Poland. Beginning with the presentation of its background, we show how particular strategies (i.e., relamping, adjusting lighting installations, and introducing control capabilities) decrease power usage and thus the GHG emission volumes. The extrapolation of results to the scale of a lighting system of the entire city is also presented. Further, we convert CO
emission volumes to the costs related to the corresponding emission allowances. We show that those costs can reach 10% of savings achieved thanks to decreased power usage. In Section 4
, we discuss obtained results. The final conclusions are contained in Section 5
2. Retrofit Strategies–State of the Art
In this section, the detailed overview of retrofit approaches enumerated in the previous section are presented. The relevant GHG emission reductions are expressed in terms of power reduction ratios as the GHG emissions can be simply obtained on this basis.
2.1. Light Sources Replacement
Replacing HID-based luminaires (in particular, the HPS sources) with LEDs is a common retrofit pattern. What is interesting, due to the significant financial benefits, the municipalities decide to install LEDs, even if the end of an HPS fixtures’ life cycle is not reached.
Typically, during an HID to LED migration, conversion charts provided by lamp vendors are used (see Table 1
). Such a chart allows finding, for a given HID lamp’s power, a rough estimation of an equivalent (in terms of a desired luminosity) LED’s wattage to be installed.
Although the unit savings shown in Table 1
are, as mentioned, only the rough estimations supplied by a specific manufacturer, they express the order of magnitude of available power reduction ratios. For the considered example, those ratios vary between 32% and 59%. It is obvious that total power savings for any real-life case will depend on both the lighting system structure, i.e., the ratios of particular fixture types being retrofitted, and the installation’s size.
2.2. Lighting Design Tunning
Power savings can be obtained thanks to a well suited lighting design. It is achieved by adjusting lamp parameters such as the fixture’s photometric solid, dimming, pole height, arm length, fixture’s mounting angles, etc. The above installation tuning may be done according to the either standard or customized design approach. In the first case, one assumes regularity and uniformity of a lighting situation, namely, a constant road width and evenly spaced luminaires (Figure 1
a). In the second approach, an actual roadway layout is assumed (Figure 1
The former method makes an installation meet performance requirements imposed by a lighting standard and minimize power usage. This approach, as a multivariate optimization, requires advanced software tools capable of solving the problem in an acceptable time. It has to be noted that imposing uniformity of a lighting situation leads to over-lighting due to the conservative assumptions on the road width and lamp spacing.
The latter design scheme, relying on the different design paradigm, was introduced in the work [15
]. In this approach, the lighting situation is regarded strictly as is, i.e., with its actual geometry recovered on the basis of GIS coordinates (Figure 1
b). In addition, the coordinates of luminaires are taken from a GIS data repository. In such an approach, the lighting situation is no longer uniform, which means that neither road width is constant nor luminaires are distributed evenly along a road. Although this method implies a significant computational overhead (one has to analyze multiple road segments rather than a single roadway, as shown in Figure 1
), the resultant installation’s configuration is designed in such a way that over-lighting is minimized. As shown in [15
], the customized design method reduces a power usage by 15% compared to the standard approach described above.
While assessing lighting class selection for a given street segment there are certain measurable coefficients that influence this process. One of them is traffic intensity, or volume. Depending on the number of vehicles passing, there could be up to three different lighting classes assigned. Varying lighting classes lead to different dimming settings of the luminaires, which results in energy savings. While assigning a lighting class, the maximum traffic volume should be considered. For such a class, power levels of the luminaires should be selected accordingly by means of photometric calculations. If information about a current traffic volume is available, then changing the classes for a given street segment can be performed efficiently [17
], which leads to energy savings [19
] and CO
presents the 24-h lighting class assignment structure and the comparison of obtained savings for various control approaches. The table is made for the arterial road with a dominant lighting class ME2.
It should be noted that the new release of the standard CEN 13201 was issued in 2014 but its Polish localization was published two years later, in 2016. For that reason, the calculations presented below was initially made in accordance with CEN 13201:2004.
The analysis was based on 100 luminaires and one year worth of traffic data. The calendar control strategy assumes that ME2 is assigned regardless of an actual traffic intensity and lighting is turned on at sunset and turned off at sunrise, totaling 4292 on-hours. The dynamic assumes reading traffic intensity every 15 min. The lighting classes are adjusted accordingly which leads to the 34% energy saving comparing to the calendar approach. ME2 is used 27% of the time, while the lower class, ME3b, is assigned 14%, and ME4a 59%.
In some situations, a statistic approach is also used. In this case, the control is not based on actual traffic volume but on statistical analysis of its historical data records—separately for working days, holidays, Saturdays, and Sundays. Even though it is commonly used, it very often leads to lighting class violations because of a high traffic variability. An actual traffic volume can surpass the statistics based on historical or incomplete data. An example for the aforementioned arterial road is also presented for comparison. For more diverse road and street infrastructure, actual savings obtained by application of dynamic control are expected to be at the level of 27% [20
The above findings regard CEN/TR 13201-1:2004. The current release of the standard (since 2014) alters number of regulations, lighting class names and their assignment methods, among others. It also introduces a notion of an adaptive control. This gives greater flexibility for dynamic control applicability. A comparison of energy savings achieved thanks to application of a dynamic control, made for a slightly larger and more representative area with variable, high volume traffic, is given in Table 3
. There are 210 luminaires installed, with the total power of 26,933 W. The total annual operation time is 4292 h. In this table, we compare the performance structure for both 2004 and 2014 releases of CEN/TR 13201-1. As can be seen, following the 2014 revision leads to significant savings, from 35% to 46%, which gives the motivation to upgrading a lighting infrastructure performance to the new release of the standard. It is mainly due to introduction of maximum traffic capacity indicator on which assignment of lighting classes is based now. The M2 class is assigned 8% of the time only.
The energy saving is also influenced by the traffic intensity parameters, in particular how often the data are read from sensors and how wide is the averaging time window [20
Several comments related to the above analysis have to be made.
Volumes of GHG emissions presented in the previous section may be slightly different for other countries due to different structure of energy sources (see [25
]) and different emission factors. For example, countries with an overwhelming share of nuclear plants in the total electricity production can have a lower CO
emission factor than countries where most electric energy is produced in the coal plants.
As mentioned previously, although lighting class and luminaire power structures of the considered case (see Figure 2
) are sufficiently representative for the presented analysis, one has to keep in mind that such an extrapolation estimates results rather than gives the precise values. Regardless, those numbers give a reasonable measure of an overall reduction of a GHG related pollution in the scale of the entire city.
The results shown in Figure 5
correspond to the roadway structure of the city of Cracow and may be subject to changes for a city having a different road network structure. Additionally, municipalities can apply other lighting class assignment policies resulting in dominance of high or low energy demanding classes.
A highly important issue for authorities planning lighting retrofit works are investment costs. One has to be aware that a final decision on retrofit strategy is usually a trade-off between financial constraints and other, non-fiscal (from the investor’s perspective) factors such as environment protection. The presented results shed light on how the latter can be taken into account when assessing an investment’s financial profitability.
In the presented considerations, we did not discuss maintenance costs of a lighting installation. They are another factor which leads to choosing LEDs [26
]. It is worth mentioning that the total maintenance cost savings achieved thanks to upgrading 300 fluorescent tubes with 100 ballasts to LEDs is over $
16,500 over an LED lifetime. For the case of HIDs, upgrading 100 bulbs to LEDs yields over $
45,000 of the total maintenance cost savings, over an LED lifetime [27
In this work, we considered how modernization of HPS-based lighting installations influences CO emission volumes related to urban roadway lighting. CO and other greenhouse gases emissions are linearly dependent on the energy usage so a parallel analysis of both was made. Our study relied on the real-life case of the retrofit made in the city of Cracow.
In our research, we show that:
The highest contribution (41%) to the emission reduction was generated by relamping, i.e. replacing HPS fixtures by LEDs. The next factor (14%) was introducing an adaptive lighting control system. An important condition which was imposed on the control was that the resultant luminaire performances had to comply with the EN 13201-2 lighting standard [13
]. In third place (8%) was preparing well suited lighting designs of installation being retrofitted.
The most demanding lighting classes (i.e., having the least indices x for Mx, Cx, and Sx) contribute the most to an overall emission reduction. It should be noted, however, that this result cannot be replicable if, for a given city, a number of lighting points illuminating low classes, say M5 or C5, is predominant.
The value of emission allowances (with the EUA price at the level of €17.00/MT) corresponding to the reduced CO emission volume, reached 10% of relevant energy savings. This rate yields the 9% reduction of a retrofit payback period.
The presented work links well defined, quantitative analysis of financial aspects of a public lighting retrofit investment with considerations on environmental influence of such a modernization. This link is obtained thanks to assessing the money savings corresponding to a reduced CO
emission. Thus the environmental impact can be included quantitatively in the retrofit strategy planning. Moreover, for investments financed in the ESCO (Energy Service Company) model [28
], we show that a payback period can be shortened by 9% (or even more, taking into account the current trend of the EUA price) thanks to incomes achieved due to the reduced CO