Cost Benefit Analysis of Adaptive Solar Control and View Management: Case study of an office building in New York City

1.1. Research Background and Purpose

Solar radiation has a significant impact on the energy performance of the building and the occupant's visual and thermal comfort often affecting their work productivity¹⁾. The purpose of solar shading is to control the amount of incoming radiation while maintaining desired visibility to the exterior. While the term "shading" refers to the protection from solar radiation, there are certain periods of time, dates or seasons, depending on the location, where incoming solar radiation is beneficial to help reduce lighting and cooling loads while increasing occupant comfort. By striking a balance between the need for daylight, visibility to the exterior, lighting, and cooling loads the term solar shading needs to be viewed in the perspective of solar control and visibility management.

In practice, solar control also known as daylighting design is primarily provided by the lighting designer as an additional service²⁾. This was also the case for the design of the case study building observed by the author. Under the AIA B201-2017 Standard form for Architect's Services[12] for design and construction management between the owner and the architect, daylighting design is considered an additional service or supplemental service. Most stake-holders, including the client, would acknowledge and recognize the importance of daylighting, solar control and visibility to the exterior for the well-being and productivity of the occupant. However, it is difficult to quantify and justify the additional cost required to install and maintain an active solar control system, which is considered above and beyond the typical manually operated interior roller shades or venetian blinds. As a result, decisions to implement active solar control in the building design are often dismissed or not even considered in the early phases of design due to a lack of quantitative information about its benefits. Obtaining this information for a bespoke project requires a significant investment of time and effort. Seeking to address this, this paper combines an empirical account of a case study in practice, and findings from previous research combined with real cost data from practice to offer an estimated cost benefit analysis of implementing solar control during the early phases of design.

1.2. Methodology and Scope of Research

Findings from previous research on quantitative benefits of adaptive solar control are coupled with data obtained from practice and applied to a case study building in New York, NY. The proposed research methodology is outlined in Fig. 1.

Fig. 1.

Research Methodology Diagram

Four categories of quantitative benefits are identified from recent literature: (1) energy savings, (2) carbon dioxide emission reduction, (3) peak load reduction, (4) glare and its impact on productivity. Each quantitative benefit is coupled with cost data obtained from practice and applied to the case study building, described in Section 3, to derive a cost / benefit analysis, see Table 2.

Table 1.

Summary of Quantitative Findings from recent literature

Table 2.

Cost/Benefit Analysis of incorporating exterior adaptive solar control

2.1. Overview of Adaptive Solar Control Systems

“Automated”, “dynamic”, or “adaptive” solar building control systems have been commercially available for the past few decades[5] and installed in more projects in Europe compared to the US due to contrasting expectations in performance, cost and quality, including concerns about operation and maintenance[5]. In residential homes or smaller buildings, the use of traditional manual blinds or shading may be sufficient. But in larger buildings such as an office building, with a multitude of occupants, a more sophisticated building control strategy involving sensors and algorithms that respond to the changing surrounding environment is preferred over manual controls of occupants[6]. Excluding smart window technology, there are two common adaptive solar control systems available, motorized blinds and roller shade systems.

Traditional manually operated venetian blind systems are a viable solution to control solar gain and glare. Wide range of controls are available through the geometry of the louvers which can be tilted manually by the occupant. However, blinds are rarely set up in an optimal manner and in the absence of the occupant or user, allows either excessive heat gain or loss. Automated blinds through sensors and controls allows for enhanced performance and occupant comfort while also providing the option for manual override.

A common alternative to blinds are roller shades which are commonly available with a wide range of fabrics and optical properties. Fabric specifications determine the percentage of visible sun and light transmittance, reflectance and absorbance which affects both visual and thermal quality in the interior environment. While roller shades can be partially or fully lowered, the view will be reduced for those time periods when shading is critically needed. In contrast, venetian blinds control solar radiation through adjustable horizontal louvers, supporting greater visibility to the exterior, of significant value in improving health and productivity for the occupants that could offset their greater first costs and maintenance costs. Common optical properties of shade fabrics include openness factor(OF), solar transmittance(Ts), solar reflectance(Rs), solar absorbance(As), and visible transmittance(Tv). In addition, due to ultraviolet degradation, exterior roller shade fabrics are further limited in availability compared to interior roller shade applications. While simpler in operation compared to blinds, roller shades are limited in optical control.

2.2. Cost / Benefit Analysis in Sustainability

“Environmental designers often argue for broad sustainability objectives without further detail...”[8]

Investment in sustainable design technologies, such as adaptive solar control systems, are often dismissed or limited due to first cost decision making. Yet, research that assesses the cost benefit or return on investment of a sustainable technology are currently limited. In practice, this type of assessments are rarely performed. In comparison with other disciplines such as automotive or computing industry, every investor is aware of the “component by component quality... including, life cycle benefits...”[8].

In early phases of design, this is exacerbated by the fact that information needed for a sophisticated analysis are often unknown. Ironically, early phases of design often has the most influence in the first cost and performance of the building. As a result, a less time consuming cost benefit analysis is needed to provide a general cost of ownership or return on investment for decision-makers and stake-holders.

2.3. Review of Previous Studies

This research relies on findings from previous research on quantitative benefits of dynamic solar control to derive a cost benefit analysis. Four categories of quantitative benefits are identified from recent research: (1) Energy savings, (2) Carbon dioxide(CO₂) emission reductions, (3) Peak load reduction, and (4) Glare and Productivity. An additional category which relates to daylight / visual connection and productivity is identified but quantitative benefits are not derived and utilized in the calculation of the cost benefit analysis.

Research on energy savings include Littlefair's[7] series of experiments to simulate(using DOE-2 with Equest interface) the performance of various solar control strategies in an office building in London. Five cases are reviewed: (1) no shading, (2) manually controlled internal shading(roller shades), (3) exterior fixed shading(overhang), (4) adaptively controlled internal shading, and (5) external shading device that is moveable and adaptively controlled. Results indicate that in terms of overall energy reduction(heating, cooling, lighting, and ventilation), the best performing solar control strategy is the automatically controlled internal roller shades as it provided a 6.6% reduction in total delivered energy when compared to no shading and 3.8% reduction when compared to internal manual roller shades. In comparison, adaptively controlled moveable exterior shades provided a 1.3% total energy reduction when compared to no shading and an increase of 1.6% when compared to internal manually operated shades. When evaluating energy reduction in terms of cooling load alone, adaptively operated exterior shades provided a 66% reduction when compared to no shading and a 53% reduction when compared with interior manually operated shades. The substantial savings in cooling energy would allow the removal of cooling in portions of the building creating further initial cost savings but this would need to be evaluated on a case by case basis.

Littlefair's[7] analysis extends further into the amount of carbon dioxide emissions for multiple shading options. Results indicate that adaptively controlled exterior shades provide a total of reduction of 7.8% in CO₂ emissions over no shading and a 3% reduction over interior manual roller shades.

Research on peak load reduction include Tzempelikos, Athienitis, and Karava's[10] research which performed an integrated thermal analysis on a 53,000m² engineering building in Montreal. Three cases were reviewed, passive lighting control with no shading, active lighting control with shading, and active lighting control coupled with adaptive venetian blinds and roller shades. The main finding of the research indicates a 512kW/37% peak cooling load savings by using adaptive solar control coupled with lighting control strategies. This amounts to a 0.00966kW peak load reduction per m².

Research on glare include Zhang and Altan's[11] survey of occupant comfort(thermal, visual, and acoustics) in a conventional high rise block and a contemporary environmentally designed building in Sheffield, UK. Survey results indicated that in a conventional high rise building with high WWR, 65.3% of occupants experienced discomfort from glare.

Research on productivity include Osterhaus and Bailey's[9] lab experiment at the Lawrence Berkeley Laboratory where computer operators were exposed to large area light source of variable but uniform luminance. Results of the experiment include a 3% reduction in visual task efficiency along with a marginally increase in error rate in high glare situations.

Finally, research on productivity tied to view management include Heschong's[2] seminal study on SMUD call center. Findings include a significant increase in productivity(7% to 12% in processed calls and 10% to 25% in mental function and memory recall) with a pleasant visual connectivity to the exterior in comparison to no view. The study was conducted with the use of solar control in the form of perforated vertical blinds which partially screened by the view[2].

3.1. Context of Case Study Building

The case study building is situated in New York City, NY, USA. New York City like many metropolitan cities is known for its skyscrapers and commercial office buildings with fully glazed facades. Recent studies focused on this trend of highly glazed facades in commercial office space[6]. However, a large segment of commercial retail space within New York City are provided by mid to low-rise buildings with limited glazed fenestrations. In fact, New York zoning laws prohibits and limits the construction of high-rise buildings in certain commercial areas. Representing the reality of building construction in practice, the case study building is a mid-rise new construction, five-story 3,250m²(35,000ft²) commercial office building in New York, NY with approximately 50 employees³⁾. The building is designed to meet New York City 2014 Energy Compliance Code 16 requirements for new construction and ASHRAE 90.1-2007 as a minimum for performance of a standard code-compliant building.

3.2. Case Study Building

The case study building was designed by an architectural design firm along with a group of consultants⁴⁾ in New York City as the new headquarter for a private company. The author was involved directly in the design of the building enclosure with input from consultants and with access to local contractors, fabricators and suppliers who were bidding on the project (project sensitive information including the names and addresses of stake-holders have been withheld).

Due to the high cost of land and space, like most buildings in New York City, the case study building maximizes it’s allowable buildable area based on local zoning laws and setbacks. The building dimension corresponds to the plot dimension of 22.8m in width and approximately 30.4m in depth with a 6m rear yard setback applied from the second floor. The overall building height is 35.4m which includes five floors and a mechanical penthouse above the roof, see Fig. 2. for a schematic building massing of the building. The ground floor of the building is an open plan commercial space while the upper floors(floors two to five) are typical office spaces for employees.

Fig. 2.

Schematic Building Massing

The structure of the building is primarily concrete(concrete beams, columns and slabs). While steel was briefly considered, due to financial reasons, concrete was chosen as the primary structure of the building. Heat and cooling is primarily provided by two air handing units located above the mechanical penthouse and distributed through centrally located shafts. Lighting is provided by a combination of LED track lighting, recessed down lights, and suspended light fixtures controlled by an overall building management system with local controls provided in central locations throughout the building.

The case study building's facade(see Fig. 3.) is the basis for analysis. The facade design consists of colored concrete block and horizontal stone bands with individual windows⁵⁾ from levels two to five. A large opening is provided at the ground level resulting in a total window to wall ratio(WWR) of 35%. From the limited choice⁶⁾ of commercially available exterior roller shade fabrics, White / White color shades are selected with a 5% openness factor, a key criterion which balances the need to control glare while maintaining visibility through the shade. Other solar optical properties of the shade fabric include: solar transmittance(Ts) of 22, solar reflectance(Rs) of 62, solar absorbance(As) of 16, and visible transmittance(Tv) of 23.

Fig. 3.

Case Study Building Elevation

Another recent specification criteria of roller shades include the View Clarity Index formula developed by Konstantzos et al.[4]. The range of values of the View Clarity Index ranges from 0 to 1 where 0 represents full blackout and 1 referring to fully visible. The proposed shade fabric has a View Clarity Index(VCI) of 0.2389 when put into perspective closely correlates to the a typical white Lutron interior shade. For a graphic representation of the proposed exterior mounted adaptive solar control and its components, see Fig. 4.

Fig. 4.

Image of proposed exterior adaptive solar control and its components in both retracted and deployed state

3.3. Cost Benefit Analysis

A cost benefit analysis is performed for the 3,250m²(35,000ft²) case study building with fifty employees in New York City. Quotes for fabrication and installation of adaptive solar shades are obtained from local installers in New York. The quote provides an initial cost of adaptive shade control(exterior roller shades) for the case study project to be $646 per m² of window area. With 24 windows, each with an area of 4.5m², and a total window area of 108m², this results in a total initial installation cost of adaptive roller shades at $69,788. With 50 employees in the case study building within 6m of the enclosure, the installation cost per employee is approximately $1,395, refer to Table 2.

Estimated total electricity consumption(cooling, lighting, water heating, and misc.) of a 3,250m² commercial office building in New York is approximately 441,256kWh⁷⁾ at an average electricity cost of $0.2217 per kWh. Heating is provided by natural gas and the approximate annual consumption is 551.5 MMBtu at $0.16 per MMBtu. This results in a total electricity cost of $97,822 and a total heating cost of $8,769 for an annual total combined energy cost of $106,591. Applying a 1.3% savings from recent research[7](see Table 1.), total annual energy savings gained from installing exterior adaptive solar shading amounts to $1,385, with a benefit per employee of $27.70, refer to Table 2.

The cost of peak demand reduction can be calculated by reviewing the cost to purchase a new generator. Banting et al.[1] calculated the cost of bringing in new generation capacity at $600 per kW while evaluating environmental benefits of implementing green roof in the city of Toronto. The total estimated peak load reduction for the case study would be 31.5kW(0.00966kW/m². x 3,250m²). With the cost of bringing new generator capacity at $600 per kW, the initial cost savings of avoiding the construction of new generator capacity would be $18,900(31.5kW x $600), a savings of $378 per employee, see Table 2.

Calculating the cost of reduction in CO₂ emissions is a three step process. First, the amount of CO₂ emissions is calculated based on energy consumption(electrical + heating) of the building. The Greenhouse Gas Equivalencies Calculator from the United States Environmental Protection Agency(EPA)⁸⁾ estimates 50kg of CO₂ is release per 0.1MMBtu of natural gas. For electricity, an average of 0.7kg CO₂ is released per kWh according to the United States Environmental Protection Agency(EPA). By applying the amount of annual natural gas and electricity used to the factors provided by the EPA, results indicate a total of 27,300kg of CO₂ emission from natural gas(551.5MMBtu x 50kg CO₂) and 304,000kg of CO₂ emission from electricity (441,256kWh x 0.7kg of CO₂) for a combined total of 331,300kg of annual CO₂ emission.

Second, the cost of CO₂ emissions is calculated. The attempt to quantify the social cost of carbon dioxide emissions has been documented in a 2010 report by multiple agencies within the United States Government[3] including but not limited to the Department of Energy, Commerce, Agriculture, Transportation, and Office of Energy and Climate Change. The intent was to "incorporate the social benefits of reducing carbon dioxide emissions into cost-benefit analyses of regulatory actions"[3]. The report provides four estimates based on three methods of calculation and a fourth calculation that accounts for "higher than expected impacts from climate change". Under the 2015 calculation, per 1,000kg of carbon dioxide emissions, the cost varies from $5.7(5% discount rate), $23.8(3% discount rate), $41.7(2.5% discount rate) and $72.8 based on a higher than expected impact from climate change. By applying these rates to the total CO₂ emission, the annual social cost of carbon dioxide emission for the case study building is estimated anywhere between $1,888 to $24,118 annually.

Third, findings from previous literature, see Table 1., is applied. 3% CO₂ emission reduction is quantified based on recent literature[7], see Table 1., resulting in an estimated annual cost savings of $57 to $723 annually, a savings of $14.46 per employee, see Table 2.

Calculating the cost benefit of glare and its resultant impact on productivity, 3% productivity gain[9] from 65.3% of employees [11] can be projected based on recent literature, see Table 1. Applying this to the case study building with 50 employees at an average salary of $40,000 this amounts to total potential productivity benefit of $39,180($783.60 per employee).

Factoring in all the cost benefits from reduced annual energy use, peak demand reduction, reduced carbon dioxide emissions, and productivity gains from glare control, a five year cost benefit analysis is represented in a graph in Fig. 5.

Fig. 5.

Five Year Cost Benefit Analysis

3.4. Analysis of Results

Analyzing the results, first year(12 months) return on investment(ROI) is projected to be 86%. A key finding in the analysis is that productivity gains account for 65% of the first year return on investment(ROI). Full return on investment is projected to occur in the 13^th to 14^th month(first quarter of Year 2). To further illustrate the significance of productivity on the return on investment, a separate analysis is performed based on productivity alone, represented in a dashed line in Fig. 5. Based on productivity alone(excluding all other benefits), full return on investment is projected to be slightly lengthened(22^nd month or third quarter of the Year 2). Excluding productivity benefits, full return on investment is extended by more than 2 years resulting in a projected return on investment occurring in the 38^th month(the first quarter of Year 4), represented by the center line type in Fig. 5.

3.5. Limitations of Research

Results from the cost benefit analysis portray an estimated return on investment and a general indication of its financial feasibility. Based on this information, clients and stake-holders are able to make an informed decisions on viability of exterior adaptive solar control during early phases of design. However, there are a few limitations with the proposed research, methodology and analysis to be considered.

A key component of the research is in the proposed methodology of utilizing quantitative benefits from recent literature. By utilizing existing research, a significant amount of time and effort is saved at the expense of obtaining an estimated quantitative finding. This approach, relies on the assumption that the referenced research, see Table 1., has a high correlation to the proposed research. To ensure this correlation, a significant amount of effort was undertaken to identify reliable existing research with similar characteristics to the case study building. Yet differences which may have an impact on the quantitative results are inevitable. For instance, Littlefair's[7] research is used to derive energy savings and CO₂ emission reduction findings by utilizing exterior adaptive solar control on a three story office building located in London. While the topic, function(office building) and the climate conditions⁹⁾ may be similar, the geometry, orientation of the building, its surrounding context, and environmental conditions may sufficiently distinct to question its applicability to New York City. However, this tradeoff may be worthwhile when considering the following alternatives.

The most common approach is to undertake simulations to obtain quantitative findings based on project specific information. This alternative has its own shortcomings during early phases of design. In early phases of design(pre-design and schematic design), the design is in its inception and still evolving. As a result, information used in performing simulations are often undecided or unknown. Therefore, even with the added investment of time, the results obtained may be equally questionable.

To avoid this issue, simulations in most cases are performed in latter phases of design(design development and contract documentation). However in the latter phases of design, many of the design decisions are already in place and therefore the results of the simulations are simply a verification of the design decisions and not a tool to evaluate its performance or financial feasibility.

Weighing the overall benefits and its shortcomings, the use of quantitative benefits from existing research, if carefully chosen to correlate with the subject of the research, may not be the most reliable option but the most optimal in balancing time and effort invested while maintaining a reasonable amount of reliability.

Cost data presented in this paper also has a few limitations based on location. Installation and first cost information was obtained from correspondence and interactions with local suppliers and non-union local installers during the design and bidding phases of the project between the years 2013-2015¹⁰⁾. Energy cost was obtained from publicly available information provided by a local energy service provider in New York City. The main limitation of the collected cost data, is in its applicability to other locations. New York City is known for its high labor rates¹¹⁾ and cost of energy when compared to other metropolitan cities. As a result, the cost data information is not representative of a typical urban condition and therefore to apply findings from this research to another geographical location, local cost information needs to be obtained and factored in.