NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

National Utility Rate Database

Preprint

S. Ong and R. McKeel

Presented at the 2012 World Renewable Energy Conference

Denver, Colorado

May 13-17, 2012

Conference Paper

NREL/CP-6A20-54633

August 2012
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1

WREF 2012: NATIONAL UTILITY RATE DATABASE

ABSTRACT

When modeling solar energy technologies and other 

distributed energy systems, using high-quality expansive 

electricity rates is essential. The National Renewable 

Energy Laboratory (NREL) developed a utility rate 

platform for entering, storing, updating, and accessing a 

large collection of utility rates from around the United 

States. This utility rate platform lives on the Open Energy 

Information (OpenEI) website, OpenEI.org, allowing the 

data to be programmatically accessed from a web browser, 

using an application programming interface (API). The 

semantic-based utility rate platform currently has record of 

1,885 utility rates and covers over 85% of the electricity 

consumption in the United States.

1. INTRODUCTION

Access to detailed utility rate data is important for a variety 

of energy-related analyses efforts. The retail price of 

electricity is the single most important factor affecting the 

economics of residential photovoltaic (PV) systems 

(Denholm et al. 2009). Electricity prices are also important 

for economic calculations of other distributed generation 

technologies, building energy management solutions, and 

electric vehicle charging.

Until the introduction of the National Utility Rate 

Database, there was no publicly available utility rate 

database (EIA 2012a). Analyses that required electricity 

rate information typically used averaged or blended rates, 

which are derived from utilities’ revenue and sales data 

(EIA 2012c). Table 1 shows an example of average 

residential rates, as reported by the Energy Information 

Administration (EIA). Any of these rates could be used for 

Colorado; however, the best choice would be the one with 

the most specific location, the Xcel Energy, the electric 

utility for Denver, Colorado.

The average residential rate for Denver shown in Table 1 

was derived from Xcel Energy’s total revenue and sales in 

Colorado. This is referred to as a blended rate, as it blends 

all charges—fixed and variable—into a single number. 

Although blended rates may be at a sufficient resolution 

for high-level analysis, it is important to have actual rate 

structure data when performing a detailed analysis in order 

obtain accurate results. Various types of utility rates are 

used throughout the United States, including: flat rates, 

seasonally varying rates, time-of-use rates, demand 

charges, and tiered or block rates (these are described in 

more detail in Section 3).

TABLE 1: TYPICAL ELECTRICITY RATE VALUES 

THAT ARE USED FOR DENVER, CO

Location Average Residential Rate in 

2010 (cents/kWh)

United States 11.5

Mountain Region 10.5

State of Colorado 11.0

Denver, CO (Xcel Energy1) 11.2

Source: EIA dataset EIA-861

 1

Within the State of Colorado, Xcel Energy legally 

operates as the Public Service Company of Colorado.

Sean Ong, Ryan McKeel

National Renewable Energy Laboratory

1617 Cole Boulevard, Golden, CO 80401

Sean.Ong@nrel.gov

Ryan.McKeel@nrel.gov
2

The various rate structure types can significantly impact 

the economics of distributed energy systems, such as solar 

installations (Ong et al. 2010). Rate structure data is 

needed in order to make informed decisions regarding 

home and building energy management2

. Prior to the 

existence of the National Utility Rate Database, energy 

analysts that needed detailed utility rate structure data had 

to collect this data from individual electric utilities. 

Collecting this data has required trained individuals to look 

through utility tariff books, which typically comprise 

several hundred pages. These efforts were repeated across 

organizations, and this data could not be shared easily 

between organizations due to the lack of a common 

standard or database.

NREL developed the National Utility Rate Database in 

2010 as a free, web-based tool in which the energy analysis 

community could contribute, edit, and download utility 

rate data. This database was developed on the OpenEI.org 

platform, where it continues to provide high-fidelity rate 

data and web services for integration with other 

applications. As of 2012, Illinois State University (ISU) 

was selected by the U.S. Department of Energy’s SunShot 

program to populate, maintain, and improve the database.

2. OPENEI PLATFORM

The OpenEI organization is a knowledge sharing online 

community dedicated to connecting people with the latest 

information and data on energy resources from around the 

world. OpenEI provides access to energy-related 

information via geographic discovery, unique 

visualizations, and topic-oriented gateways (OpenEI A). 

OpenEI is built on the MediaWiki and Drupal platforms 

and is open to the public to add and edit both content and 

data.

The OpenEI wiki holds over 56,000 pages (OpenEI B) 

which store narrative content and structured data. All 

structured data on OpenEI is available via web services 

using the semantic MediaWiki built-in query language 

(OpenEI C). The OpenEI datasets section has data and 

 2 For example, in places that use time-of-use (TOU) rates, 

it is more cost effective to run a dishwasher or dryer at 

night than it is during the day. It is also more cost effective 

to charge electric vehicles during hours when electricity 

prices are low.

references for over 800 energy datasets. In contrast to 

governmental websites such as Data.gov, OpenEI hosts 

datasets from any source around the world, and is not 

limited to datasets from government agencies.

OpenEI’s underlying emphasis on structured data and web 

services forms the basis for direct consumer access to 

energy data through the OpenEI website and through 

applications built on OpenEI. Sample questions that 

OpenEI can answer programmatically include:

• “How many utility companies are located in the 

United States?”

• “What is the solar potential for India in megawatt 

hours per year?”

• “What financial incentives are available to Texas 

homeowners looking to purchase energy efficient 

appliances?”

• “What is a fuel cell?”

• “What energy organizations exist in the San Francisco 

Bay area?”

Crowd-sourcing is a core operating philosophy of OpenEI. 

The Merriam Webster dictionary defines crowd sourcing 

as “the practice of obtaining needed services, ideas, or 

content by soliciting contributions from a large group of 

people and especially from the online community rather 

than from traditional employees or suppliers.” (Merriam 

Webster)

More than 3,000 visitors to OpenEI have created an 

account, performing community functions like editing and 

creating pages. To seed structural data on OpenEI, bots3

are used to populate pages and linkages between concepts. 

To avoid re-creating content that is available elsewhere, 

OpenEI directly extracts content from other semanticallyenabled websites. For instance, the “India Energy 

Resources” page on OpenEI pulls data from a growing 

repository of energy maps related to India and the websites 

Wikipedia.org and REEGLE.info (OpenEI D). This 

enables visitors to browse organized information about 

energy topics from around the web without concern about 

accessing out of date or duplicate data.

Generating and maintaining an accurate database of 

electric utility rates is a difficult problem to solve. There is 

no central repository for utility rate data (EIA 2012a), and 

 3

A bot is an automated process that performs specific tasks.
3

data collection efforts are further complicated due to 

periodic changes to rate structures. With no centralized 

reporting mechanism or structure, this data often resides in 

a non-machine readable document format. A desire for 

accurate, structured utility rate data lends itself to an input 

mechanism that harnesses crowd sourcing.

Users of utility rate data may wonder, “How can this 

database be open to the public for creation and editing, yet 

maintain a high-level of data quality?” In the case of the 

National Utility Rate Database, subject matter experts are 

the data wardens. When a new entry is created, a subject 

matter expert receives an email or notification by RSS 

feed, suggesting a review. Data that has been verified by a 

subject matter expert indicates an “approved” status. Data 

that have not been verified will still be accessible to the 

public, but is labeled as “not yet approved.” 

In 2010, energy analysts at NREL identified the utility rate 

data on OpenEI as a high-value digital asset. The OpenEI 

developers integrated the database into a web service for 

use by external applications. The National Renewable 

Energy Laboratory’s System Advisor Model (SAM) was 

the first application to utilize this web service, and added 

capabilities to download data directly from the National 

Utility Rate Database. As of this writing. several other 

organizations are reviewing the feasibility of using this 

web service to programmatically access the National 

Utility Rate Database.

3. TYPES OF ELECTRIC UTILITY RATES

Various types of utility rates are used throughout the 

United States. The most common rate types (Ong et al. 

2011) include the following:

• Flat rates. Fixed cost of energy that does not vary 

except for fuel cost adjustments and other fees. 

• Seasonal rates. Rates vary by season. A typical 

seasonal rate structure has a lower rate for winter 

months and a higher rate for summer months.

• Time-of-use rates. Time-of-use (TOU) or time-of-day 

rate structures usually vary 2–4 times a day. A typical 

TOU rate has a lower cost at night, a higher cost 

during the late afternoon, and an intermediate cost 

during the mornings and evenings. The term “onpeak” 

or “peak” is generally used to describe hours with 

higher prices while “off-peak” is used to describe 

hours with lower prices.

• Demand charges. Normally included with energy 

charges in applicable rate structures, demand charges 

charge customers for their peak power (kilowatts) 

usage. Demand charges can also be fixed or vary by 

season or hour.

• Tiered or block rates. Tiered rates typically refer to 

rates that increase with increasing electricity usage 

while block rates typically refer to rates that decrease 

with increasing electricity usage. These rates are most 

common in the form of energy charges; however, 

tiered demand charges are also used.

• Database Statistics

This section describes the status of the National Utility 

Rate Database as of March 13, 2012.

Through crowdsourcing and data seeding techniques, over 

500 utility rates were added to the National Utility Rate 

Database from the time of its launch in 2010, until the end 

of 2011. From January 1st, 2012 to March 13th, 2012, over 

1,300 new utility rates were added, largely ISU in response 

to their SunShot award from the Department of Energy 

(DOE 2012). In total, the database contains data for 1,885 

utility rates.

TABLE 2: ELECTRIC RATE STRUCTURE TYPES 

SUPPORTED IN THE DATABASE.

Rate Structure Type Supported in Utility 

Rate Database

Flat Rates Yes

Seasonally Varying Rates Yes

Time-of-Use (TOU) Rates Yes

Tiered / Block Rates Yes

Tiered TOU Rates No (planned 2013)

Tiered Demand Charges No (planned 2013)

Reactive Power (kVAR) 

Charges

No 

The 1,885 rates in the database represent 430 utility 

companies. There are over 3,300 utilities in the United 

States, but only a subset of these serve the majority of the 
4

nation’s electricity load (EIA 2012c). Initial data collection 

efforts focused primarily on the largest utilities, so the 

National Utility Rate Database contains data that cover 

85% of all electricity served in the United States. Figure 1 

illustrates the current coverage of utilities within the Utility 

Rate Database. Of the 430 utilities in the database, 40% 

have one or two rate entries, 38% have between three and 

six rate entries, and 22% have more than six entries.

OpenEI synchronizes its database of utility company 

names with the EIA’s Form EIA-861 data (EIA 2012c). 

Utility rate data on OpenEI can be queried by the name of 

the utility company or the EIA utility identification 

number, which is a unique identifier for each utility 

company. Utility companies and their rates can also be 

searched for by zip code. Additionally, a universally 

unique identifier (UUID) is automatically assigned to each 

utility rate to ease scalability and eliminate issues with 

community-generated titles for utility rates. Thus, a utility 

rate is identified by a UUID instead of the typical page title 

as a unique identifier. Sample queries and documentation 

for OpenEI utility rate data are located at 

http://en.openei.org/services/.

Fig. 1: This map shows utility rates entered into OpenEI as of March 13, 2012.
5

Fig. 2: Contributed utility rates by end-use sector.

The rates are categorized both by end-use sector and by 

rate structure type. Figure 2 illustrates the portion of 

utility rates in each sector. Some utility rate entries do not 

specify a sector, which likely indicates that the rate entry 

is incomplete and pending further subject matter review.

Figures 3a and 3b illustrate the breakdown of rate 

structure type by residential and commercial sectors. 

Tiered rates are the most common structure for residential 

rates, while rate types are more evenly distributed in the 

commercial sector.

Fig. 3: Rate structure types by residential and commercial sectors.
6

4. DATABASE USAGE AND FUTURE EFFORTS

Since its release, the Utility Rate Database has been 

instrumental in a variety of analyses efforts. A recent 

project, funded by the DOE Solar America Showcase 

program (DOE 2012b), explored the economics of rooftop 

PV system on schools in California (Ong, et al. 2011). The 

database was a crucial component to the analysis; the 

analysis focused on the impacts of choosing optimal rate 

structures. Results from the analysis (see Fig. 4) showed 

that system economics were highly sensitive to the rate 

structures and provided insights on how optimal rate types 

can be identified.

A variety of other analyses efforts that rely on the database 

are currently in progress at NREL. These include projects 

that evaluate the impact of utility rates on various building 

types throughout the United States and projects that 

explore the grid-parity of solar PV in the United States.

With the SunShot award, ISU will be improving the Utility 

Rate Database by adding additional capabilities that will 

allow it to be compatible with a wider variety of rate 

structure types. ISU will also populate and maintain the 

database, with a goal of 100% coverage of U.S. rates 

within the next three years. ISU will leverage expertise 

from within its Institute for Regulatory Policy Studies to 

provide long-term solutions for maintaining the data.

Fig. 4: Results from an analysis of PV systems on school 

rooftops show that rates significantly impact system 

economics.

5. CONCLUSION 

When modeling solar energy technologies and other 

distributed energy systems, using applicable electricity 

rates is essential to accurately assess system economics. In 

response to the need for high-quality expansive utility rate 

data, a utility rate platform was developed for entering, 

storing, updating, and accessing a large collection of utility 

rates from around the United States. Since its debut in 

2010, the Utility Rate Database has been utilized in several 

analyses efforts. It has been integrated into external 

applications, where rates are able to be downloaded for use 

in computer models and tools.

The National Utility Rate Database currently contains data 

for utility companies that serve 85% of all electricity in the 

United States. Through efforts from ISU, NREL, and the 

United States Department of Energy, improvements and 

development of the Utility Rate Database continue, with a 

goal of 100% U.S. coverage within three years.

6. REFERENCES

OpenEI A. (2012). “Energy Information, Data, and other 

Resources | OpenEI.” Open Energy Information (OpenEI). 

http://en.openei.org/. Accessed March 13, 2012.

OpenEI B). (2012). “Statistics | Open Energy Information.” 

OpenEI. http://en.openei.org/wiki/Special:Statistics. 

Accessed March 13, 2012.

OpenEI C. (2012). “Semantic search | Open Energy 

Information.” OpenEI.

http://en.openei.org/wiki/Special:Ask. Accessed March 8, 

2012.

OpenEI D. (2012). “India: Energy Resources | Open 

Energy Information.” OpenEI. 

http://en.openei.org/wiki/India. Accessed March 13, 2012.

Merriam Webster. (2012). “Crowdsourcing - Definition 

and More from the Free Merriam-Webster Dictionary.” 

http://www.merriamwebster.com/dictionary/crowdsourcing. Accessed March 8, 

2012.

EIA A. (2012). “Does EIA publish electric utility rate, 

tariff, and demand charge data? - FAQ - U.S. Energy 

Information Administration (EIA).” 

www.eia.gov/tools/faqs/faq.cfm?id=20&t=3. Accessed 

March 8, 2012.
7

EIA B: Energy Information Administration (EIA). (2012). 

“Electric Power Industry--Utility Contacts.” EIA. 

http://www.eia.gov/cneaf/electricity/utility/utiltabs.html. 

Accessed March 8, 2012.

EIA C: (2012). “Annual Electric Utility Data - EIA-861 

Data File.” EIA.

http://205.254.135.24/cneaf/electricity/page/eia861.html. 

Accessed March 8, 2012.

DOE A (2012). “SunShot Initiative: Solar Projects to 

Reduce Non-Hardware Balance of System Costs.” U.S. 

Department of Energy (DOE). 

www1.eere.energy.gov/solar/sunshot/nonhardware_bos.ht

ml. Accessed March 8, 2012.

DOE B (2012). “2009 Solar America Showcases.” U.S. 

Department of Energy (DOE). 

http://www1.eere.energy.gov/solar/2009_showcases.html. 

Accessed March 21, 2012.

SAM (2012). “System Advisor Model (SAM).” National 

Renewable Energy Laboratory (NREL). 

https://sam.nrel.gov/. Accessed March 8, 2012.

Denholm, P.; Margolis, R.M.; Ong, S.; Roberts, B. (2009). 

Break-Even Cost for Residential Photovoltaics in the 

United States: Key Drivers and Sensitivities. NREL.

NREL/TP-6A2-46909. Golden, CO. 

Ong, S.; Denholm, P. (2011). Maximizing the Value of 

Photovoltaic Installation on Schools in California: 

Choosing the Best Electricity Rates. NREL. NREL/TP6A20-51694. Golden, CO.

Ong, S.; Denholm, P.; Doris, E. (2010). The Impacts of 

Commercial Electric Utility Rate Structure Elements on 

the Economics of Photovoltaic Systems . NREL.

NREL/TP-6A2-46782. Golden, CO.