As the demand fordata center capacitygrows around the world, so too grows the need among data center owners, developers, and operators to find better ways to improve the economic performance as well as the energy efficiency of their facilities. According to a “Green Data Centers” report by Pike Research,1the global market for green data centers segment of the industry is expected to more than double in size in the next four years. Underscoring that trend are announcements in recent months of large green data center investments by such bellwether companies as Apple, Microsoft, and IBM. As the industry has grown, so too has the concern that the data center industry may face regulatory pressure related to energy consumption. Some utilities are already expressing concerns about having enough power in the future to feed the number of data centers expected in their regions. Owners are justifiably seeking to discover new and better ways to improve their sustainable performance with reliable analytical methods and tools.


This article describes an economic modeling approach being used to help data center owners more wisely choose locations for new data center developments and to forecast costs along with long-term economic returns on investment associated with those prospective sites.

The data center site selection process has traditionally been influenced by such qualitative factors as personal preference, economic development incentives, or economic projections. The analytical approach described in this article avoids such pitfalls by applying a more comprehensive and quantitative means of evaluating potential data center sites.

The methodology developed applies a multi-variable approach measuring the impact of site location on a data center’s cost expressed in net present value and environmental performance. It calculates the impact site choices have on a project’s schedule and ability to be sustainable, evaluates options to incorporate on-site energy production, and factors in potential public and private incentives related to sites and other key decision-making drivers. The approach also evaluates such data center sustainability site factors as carbon usage (CUE), water usage (WUE), and power usage (PUE). This approach has enabled reductions of up to 70% in some of these categories. The methodology is also designed to perform in multi-national site comparison scenarios.

Any advanced planning methodology of this kind must use validated data rather than hypothetical inclinations. Accordingly, the intent of models such as our “Opportunity Mapping” macro-analysis methodology is to replace subjective site selection decision-making with an objective data-driven process. The Opportunity Mapping methodology uses a geographic information system (GIS) approach of layering and weighing numerous site criteria with our Data Center Site Analysis Model, which applies a data-driven methodology to analyze prospective sites and forecast owner costs and economic returns on investment associated with specific prospective sites.


Experience has shown that owners who rely on qualitative preference rather than quantitative site selection criteria run a greater risk of being disappointed in the end result. Craig Harrison, the developer of Colorado’s Niobrara Energy Park in Weld County, CO, has been a vocal proponent of using data-driven validation for green data center development decisions. He underwrote a comprehensive economic comparison of already established data center markets in 11 U.S. states. His analysis objectively compares a range of factors including economic development incentive packages, energy costs and taxes. The analytical model uses a 200,000-sq-ft data center entailing $240 million in construction along with $500 million in hardware at each compared location.

Data applied in these models include construction costs, operating labor costs, renewable energy resources, incentives and special enterprise zones, workforce availability and qualifications, taxes, utility rates, logistics and transportation, network connectivity, transportation infrastructure, environmental and regulatory restrictions, weather, geology, land and building costs, environmental factors, proximity to raw materials, and markets and special environmental considerations.

Externally derived data are used to develop site-specific conclusions related to such factors as climate, utilities, natural hazards, regulatory issues, and economic development incentives. Examples of specific sources for such data include local utilities, the National Renewable Energy Laboratory (NREL), U.S. Geological Survey (USGS), U.S. Nuclear Regulatory Commission, Federal Energy Regulatory Commission (FERC), National Oceanic and Atmospheric Administration (NOAA), Energy Information Administration, and various regional offices of economic development and trade.

Data-driven decision-making is imperative for owners seeking to reduce their facilities’ resource consumption and energy-related costs. The goal of this economic modeling methodology is to provide a clear choice of a site for data center development. An example of using this Opportunity Mapping economic method was the data-driven analysis done for Colorado’s Niobrara Energy Park (NEP).


Natural disasters can be a significant threat to any data center. The NEP site evaluation included reviewing seismic activity based on data from the USGS National Seismic Hazard Map. To determine the frequency and potential impact to the site of severe wind, tornado, hail damage, and other historic weather events, data from NOAA’s Annual Severe Weather Report Summary were analyzed. Wildfire hazards as well as fuel sources (e.g., grasses) for wild fires were evaluated. Flood hazards based on 100- and 500-yr flood plains were reviewed and overlaid on the site to assist in master planning to develop locations of buildings and critical infrastructure.

Manmade disasters can also be a significant threat. Data were gathered and reviewed to understand NEP’s proximity to both active and inactive nuclear power plants and predicted fallout zones based on prevailing winds. Beyond disasters, the site was evaluated using topological maps to determine suitable building locations and flexibility for accommodation of future development, as well as natural security features. NEP was determined to be located in a low seismic hazard area, with minimal impacts by severe weather and wildfires and located outside of flood plains and manmade disaster areas.

The Opportunity Mapping methodology also evaluates site assets including electricity, network, natural gas, water, and wastewater. Site proximity to inexpensive electrical utility sources is vital for operations of any data center. It was determined that NEP was in close proximity to three large transmission-level electrical lines. The existing substation has available capacity, which can also be expanded. Additionally, opportunity exists to build a new large capacity substation that connects to the transmission lines. Locations of primary and secondary fiber networks were defined. Networks were evaluated for capacity, redundancy, and ease of network connectivity.

This evaluation determined that NEP’s location near one of the largest gas hubs in the nation provided access to some of the lowest spot gas prices in the country. Given the proximity to the hub and the resulting low fuel prices, the site lends itself to the construction of a natural gas-fired power plant to produce electricity. Water sources were evaluated for flow and quality to determine their potential for data center cooling. Wastewater discharge options are considered based on anticipated volumes and profiles for various data center cooling options. These discharge options were then reviewed against local discharge permit requirements to determine allowable disposal methods.


Data centers are significant consumers of energy. Efforts to reduce energy consumption by utilizing free-cooling opportunities based on local climate conditions can provide significant operational savings. A site’s climate is evaluated using historic psychometric data and comparing the results to the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Class 1 recommended or allowable data center operational criteria, to determine potential energy savings through use of economizer cooling. As an illustration of this strategy, the NEP site was determined suitable for use of a cost-effective method of using external air to assist in the cooling the data center.

An even greater savings can be realized by using renewable energy sources to offset consumption from the local utility. Weather and wind data were evaluated for the NEP site. Analysis indicated that the potential for reliable wind-electric generation was substantial and that the NEP location was ideal for this renewable resource. Solar energy opportunities were studied to determine the potential capacities of photovoltaic farms as well as their optimal locations on the property. Application of fuel cell technology is also a possibility at the NEP site due to the nearby natural gas hub and inexpensive fuel. Storage of renewable energy is a challenge; therefore, sites are also evaluated for large-scale energy storage opportunities such as compressed air and thermal storage.

Due to the significant availability of renewable resources, the NEP site is a prime location for using microgrid technologies. This technology forecasts and manages energy generation and consumption. Capacities from renewable resources, natural gas, and traditional electric utilities are forecast against the critical load. A dispatch planning program determines the most cost-effective mix of power generation constrained around maximizing renewable resources and a schedule is created for the following day. The dispatch program then executes the schedule on the following day, adjusting in real time to compensate for deviations from forecasted load or for unplanned events.


The Opportunity Mapping methodology researches business incentives from utility providers; grants from county, state and federal agencies; exemptions from sales and use taxes; and property tax abatement. Additionally, financial assistance programs, low-cost financing, development bonds, and tax credits are reviewed for applicability. All of these programs help define a community’s business environment and its willingness to accept and support new business from data center opportunities.

Some of the more obscure site assets, than the ones identified above, are proximity to a skilled workforce and transportation including highways, airports, and railroads. The methodology related to these determining factors identifies nearby concentrations of workers in cities and towns as well as the availability of education for a regional workforce. Universities, colleges, and other high-technology companies are located and evaluated against the requirements of the data center. Highway access to the site as well as other regional travel infrastructure (e.g., rail and air) are considered to indicate ease of site access as well as site proximity to local communities as criteria related to a workforce’s inclination to be employed at the facility.

Site analysis is not complete until regulatory and permitting requirements have been analyzed. Storm water and wastewater discharge as well as air emissions must be in compliance with local, state, and federal regulations. Site locations are compared to air pollution attainment and nonattainment maps to determine impacts by stringent and lengthy air permitting processes. This method reviews the requirements, financial impacts of compliance, and potential schedule impacts.

Upon the validation of site assets and associated capacities, and with a full understanding of financial impacts and regulatory requirements, master planning and data center concepts can be developed that utilize assets to the fullest extent. These concepts can be further refined to define project costs, data center IT load, projected cooling requirements, and associated PUE, WUE, and CUE metrics for which solid, data-driven comparisons and decisions can be made for site selection.


Another example of Opportunity Mapping was the data-driven analysis done for the Lefdal Mine Data Center located in Måløy, Norway. This project went through the same extensive evaluation process as NEP. The Lefdal mine is an inactive olivine mine. Olivine is a magnesium iron silicate mineral, which is typically olive-green in color and when found in gem quality is known as peridot. Industrial quality olivine is used as an industrial abrasive or as refractory sand (it resists high temperatures). The olivine was extracted using a room and pillar technique, which leaves pillars of untouched material to support the roof and the extracted areas become the rooms.

The results of the evaluation process indicated that the Lefdal mine is an excellent location for a data center complex. There is no underground gas from the ore and the mine offers natural protection from electromagnetic pulse (EMP) waves, making these large rooms an ideal location to house high-value data centers as well as supporting mechanical and electrical infrastructure. Other site assets include a large, cold fjord that can be used for cooling. The fjord is ice-free and fed by four different glaciers, thereby maintaining a constant temperature. Conceptual cooling utilizes a seawater circulation system with a heat exchanger and another water circulation system within the complex. Norway’s national fiber backbone is located near the mine and is in close proximity to wind and hydroelectric renewable power sources. Wind power from nearby wind farms has already proven itself a viable source and an early projection of hydroelectric power capacity is in the range of 6 terawatts. The Lefdal site is located near three major airports and is near a large harbor and shipping port. Road access conforms to European standards for deliveries of construction and production materials.

Understanding the Lefdal site's assets and capacities allowed for master planning and data center concepts to be developed, which utilized the cold fjord and significant adjacent renewable energy resources. These concepts helped to define project costs, critical IT load, and cooling requirements along with other data center metrics allowing for objective site selection.


While this Opportunity Mapping methodology is excellent for large data center developments such as NEP and Lefdal, the same methodology can be used for smaller assessments. For example, evaluation of the potential for combined heat and power (CHP) of which both power and heat are produced from a single fuel source for data center power and cooling. The heat recovered from the production of power can be utilized in an absorption chiller to produce chilled water for data center cooling. Technologies used for power production can include reciprocating engines, gas turbines, microturbines, and fuel cells. By developing financial models, which incorporate location and climates, utility rates, local incentives, and projected electrical demands, impacts to capital and operating expenses can be quantified. This allows for technology comparisons as well as site comparisons using objective financial and performance metrics.

By considering the economic modeling methodology presented, data center owners can compare technologies, evaluate locations for site selections, evaluate environmental performance, estimate financial impacts and assistance programs, and wisely forecast long-term investments. Objective, quantifiable, and reliable net present value costs of a data center can be developed, thereby avoiding the pitfalls of qualitative evaluation. 



1.“Green Data Centers IT Equipment, Power and Cooling Infrastructure, and Monitoring and Management Tools: Global Industry Drivers, Market Analysis and Forecasts.” Pike Research. April 23, 2012.