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DOE OSTI2016

DNA Everywhere. A Guide for Simplified Environmental Genomic DNA Extraction Suitable for Use in Remote Areas

Collecting field samples from remote or geographically distant areas can be a financially and logistically challenging. With participation of a local organization where the samples are originated from, gDNA samples can be extracted from the field and shipped to a research institution for further processing and analysis. The ability to set up gDNA extraction capabilities in the field can drastically reduce cost and time when running long-term microbial studies with a large sample set. The method outlined here has developed a compact and affordable method for setting up a “laboratory” and extracting and shipping gDNA samples from anywhere in the world. This white paper explains the process of setting up the “laboratory”, choosing and training individuals with no prior scientific experience how to perform gDNA extractions and safe methods for shipping extracts to any research institution. All methods have been validated by the Andersen group at Lawrence Berkeley National Laboratory using the Berkeley Lab PhyloChip.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Greening the Grid: Pathways to Integrate 175 Gigawatts of Renewable Energy into India’s Electric Grid, Vol. 1. National Study

The use of renewable energy (RE) sources, primarily wind and solar generation, is poised to grow significantly within the Indian power system. The Government of India has established a target of 175 gigawatts (GW) of installed RE capacity by 2022, including 60 GW of wind and 100 GW of solar, up from 29 GW wind and 9 GW solar at the beginning of 2017. Using advanced weather and power system modeling made for this project, the study team is able to explore operational impacts of meeting India’s RE targets and identify actions that may be favorable for integration. Our primary tool is a detailed production cost model, which simulates optimal scheduling and dispatch of available generation in a future year (2022) by minimizing total production costs subject to physical, operational, and market constraints. Our team comprises a core group from the Power System Operation Corporation, Ltd. (POSOCO), which is the national grid operator (with representation from the National, Southern, and Western Regional Load Dispatch Centers) under Ministry of Power, National Renewable Energy Laboratory (NREL), and Lawrence Berkeley National Laboratory (Berkeley Lab), and a broader modeling team that includes Central Electricity Authority (CEA), POWERGRID (the central transmission utility, CTU), and State Load Dispatch Centers in Maharashtra, Gujarat, Tamil Nadu, Karnataka, Rajasthan, and Andhra Pradesh. Our model includes high-resolution wind and solar data (forecasts and actuals), unique properties for each generator, CEA/CTU’s anticipated buildout of the power system, and enforced state-to-state transmission flows. Assuming the fulfillment of current efforts to provide better access to the physical flexibility of the power system, we find that power system balancing with 100 GW of solar and 60 GW of wind is achievable at 15-minute operational timescales with minimal RE curtailment. This RE capacity meets 22% of total projected 2022 electricity consumption in India with annual RE curtailment of 1.4%, in line with experiences in other countries with significant RE penetrations (Bird et al. 2016). Changes to operational practice can further reduce the cost of operating the power system and reduce RE curtailment. Coordinating scheduling and dispatch over a broader area is the largest driver to reduce costs, saving INR 6300 crore (USD 980 million) annually when optimized regionally. Lowering minimum operating levels of coal plants (from 70% to 40%) is the biggest driver to reduce RE curtailment—from 3.5% down to 0.76%. In fact, this operating property is more influential than faster thermal generation ramp rates in lowering the projected levels of curtailment. While this study does not answer every question relevant to planning for India’s 2022 RE targets, it is an important step toward analyzing operational challenges and cost saving opportunities using state-of-the-art power system planning tools. Further analysis can build upon this basis to explore optimal renewable resource and intrastate transmission siting, system stability during contingencies, and the influence of total power system investment costs on customer tariffs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

U.S. Renewables Portfolio Standards: 2017 Annual Status Report

Berkeley Lab’s annual status report on U.S. renewables portfolio standards (RPS) provides an overview of key trends associated with U.S. state RPS policies. The report, published in slide-deck form, describes recent legislative revisions, key policy design features, compliance with interim targets, past and projected impacts on renewables development, and compliance costs. The 2017 edition of the report presents historical data through year-end 2016 and projections through 2030. Key trends from this edition of the report include the following: -Evolution of state RPS programs: Significant RPS-related policy revisions since the start of 2016 include increased RPS targets in DC, MD, MI, NY, RI, and OR; requirements for new wind and solar projects and other major reforms to the RPS procurement process in IL; and a new offshore wind carve-out and solar procurement program in MA. -Historical impacts on renewables development: Roughly half of all growth in U.S. renewable electricity (RE) generation and capacity since 2000 is associated with state RPS requirements. Nationally, the role of RPS policies has diminished over time, representing 44% of all U.S. RE capacity additions in 2016. However, within particular regions, RPS policies continue to play a central role in supporting RE growth, constituting 70-90% of 2016 RE capacity additions in the West, Mid-Atlantic, and Northeast. -Future RPS demand and incremental needs: Meeting RPS demand growth will require roughly a 50% increase in U.S. RE generation by 2030, equating to 55 GW of new RE capacity. To meet future RPS demand, total U.S. RE generation will need to reach 13% of electricity sales by 2030 (compared to 10% today), though other drivers will also continue to influence RE growth. -RPS target achievement to-date: States have generally met their interim RPS targets in recent years, with only a few exceptions reflecting unique state-specific policy designs. -REC pricing trends: Prices for renewable energy certificates (RECs) used to meet general RPS obligations fell in most markets in 2016, as surplus RPS supplies emerged in many regions. Price trends for solar RECs were more varied, with a particularly pronounced drop in MD. -RPS compliance costs and cost caps: RPS compliance costs totaled $3.0 billion in 2015 (the most-recent year for which relatively complete data are available), which equates to 1.6% of average retail electricity bills in RPS states. Though total U.S. RPS compliance costs rose from 2014, future cost growth in most RPS states will be capped by cost containment mechanisms.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Exploring Demand Charge Savings from Commercial Solar

Commercial retail electricity rates commonly include a demand charge component, based on some measure of the customer’s peak demand. Customer-sited solar PV can potentially reduce demand charges, but the magnitude of these savings can be difficult to predict, given variations in demand charge designs, customer loads, and PV generation profiles. Moreover, depending on the circumstances, demand charges from solar may or may not align well with associated utility cost savings. Lawrence Berkeley National Laboratory (Berkeley Lab) and the National Renewable Energy Laboratory (NREL) are collaborating in a series of studies to understand how solar PV can reduce demand charge levels for a variety of customer types and demand charges designs. Previous work focused on residential customs with solar. This study, instead, focuses on commercial customers and seeks to understand the extent and conditions under which rooftop can solar reduce commercial demand charges. To answer these questions, we simulate demand charge savings for a broad range of commercial customer types, demand charge designs, locations, and PV system characteristics. This particular analysis does not include storage, but a subsequent analysis in this series will evaluate demand charge savings for commercial customers with solar and storage.

14 SOLAR ENERGY↗
DOE OSTI2017

LBNL Laboratory Directed Research and Development Program FY2016

The Berkeley Lab Laboratory Directed Research and Development Program FY2016 report is compiled from annual reports submitted by principal investigators following the close of the fiscal year. This report describes the supported projects and summarizes their accomplishments. It constitutes a part of the LDRD program planning and documentation process that includes an annual planning cycle, project selection, implementation and review.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Tracking the Sun 10: The Installed Price of Residential and Non-Residential Photovoltaic Systems in the United States

Berkeley Lab’s Tracking the Sun report series is dedicated to summarizing trends in the installed price of grid-connected, residential and non-residential systems solar photovoltaic (PV) systems in the United States. The present report, the tenth edition in the series, focuses on systems installed through year-end 2016, with preliminary data for the first half of 2017. The report provides an overview of both long-term and more-recent trends, highlighting key drivers for installed price declines over different time horizons. The report also extensively characterizes the widespread variability in system pricing, comparing installed prices across states, market segments, installers, and various system and technology characteristics. The trends described in this report derive from project-level data collected by state agencies and utilities that administer PV incentive programs, solar renewable energy credit (SREC) registration systems, or interconnection processes. In total, data for this report were compiled and cleaned for more than 1.1 million individual PV systems, though the analysis in the report is based on a subset of that sample, consisting of roughly 630,000 systems with available installed price data. The full underlying dataset of project-level data (excluding any confidential information) is available in a public data file, for use by other researchers and analysts.

14 SOLAR ENERGY↗
DOE OSTI2017

Solar + Storage Synergies for Managing Commercial-Customer Demand Charges

Demand charges, which are based on a customer’s maximum demand in kilowatts (kW), are a common element of electricity rate structures for commercial customers. Customer-sited solar photovoltaic (PV) systems can potentially reduce demand charges, but the level of savings is difficult to predict, given variations in demand charge designs, customer loads, and PV generation profiles. Lawrence Berkeley National Laboratory (Berkeley Lab) and the National Renewable Energy Laboratory (NREL) are collaborating on a series of studies to understand how solar PV can impact demand charges. Prior studies in the series examined demand charge reductions from solar on a stand-alone basis for residential and commercial customers. Those earlier analyses found that solar, alone, has limited ability to reduce demand charges depending on the specific design of the demand charge and on the shape of the customer’s load profile. This latest analysis estimates demand charge savings from solar in commercial buildings when co-deployed with behind-the-meter storage, highlighting the complementary roles of the two technologies. The analysis is based on simulated loads, solar generation, and storage dispatch across a wide variety of building types, locations, system configurations, and demand charge designs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Crosscut report: Exascale Requirements Reviews, March 9–10, 2017 – Tysons Corner, Virginia. An Office of Science review sponsored by: Advanced Scientific Computing Research, Basic Energy Sciences, Biological and Environmental Research, Fusion Energy Sciences, High Energy Physics, Nuclear Physics

The mission of the U.S. Department of Energy Office of Science (DOE SC) is the delivery of scientific discoveries and major scientific tools to transform our understanding of nature and to advance the energy, economic, and national security missions of the United States. To achieve these goals in today’s world requires investments in not only the traditional scientific endeavors of theory and experiment, but also in computational science and the facilities that support large-scale simulation and data analysis. The Advanced Scientific Computing Research (ASCR) program addresses these challenges in the Office of Science. ASCR’s mission is to discover, develop, and deploy computational and networking capabilities to analyze, model, simulate, and predict complex phenomena important to DOE. ASCR supports research in computational science, three high-performance computing (HPC) facilities — the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory and Leadership Computing Facilities at Argonne (ALCF) and Oak Ridge (OLCF) National Laboratories — and the Energy Sciences Network (ESnet) at Berkeley Lab. ASCR is guided by science needs as it develops research programs, computers, and networks at the leading edge of technologies. As we approach the era of exascale computing, technology changes are creating challenges for science programs in SC for those who need to use high performance computing and data systems effectively. Numerous significant modifications to today’s tools and techniques will be needed to realize the full potential of emerging computing systems and other novel computing architectures. To assess these needs and challenges, ASCR held a series of Exascale Requirements Reviews in 2015–2017, one with each of the six SC program offices,1 and a subsequent Crosscut Review that sought to integrate the findings from each. Participants at the reviews were drawn from the communities of leading domain scientists, experts in computer science and applied mathematics, ASCR facility staff, and DOE program managers in ASCR and the respective program offices. The purpose of these reviews was to identify mission-critical scientific problems within the DOE Office of Science (including experimental facilities) and determine the requirements for the exascale ecosystem that would be needed to address those challenges. The exascale ecosystem includes exascale computing systems, high-end data capabilities, efficient software at scale, libraries, tools, and other capabilities. This effort will contribute to the development of a strategic roadmap for ASCR compute and data facility investments and will help the ASCR Facility Division establish partnerships with Office of Science stakeholders. It will also inform the Office of Science research needs and agenda. The results of the six reviews have been published in reports available on the web at http://exascaleage.org/. This report presents a summary of the individual reports and of common and crosscutting findings, and it identifies opportunities for productive collaborations among the DOE SC program offices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Income Trends of Residential PV Adopters: An analysis of household-level income estimates [PowerPoint presentation]

The residential photovoltaic (PV) market has expanded rapidly over the past decade, but questions exist about how equitably that growth has occurred across income groups. Prior studies have investigated this question but are often limited by narrow geographic study regions, now-dated analysis timeframes, or coarse estimates of PV-adopter incomes. At the same time, a spate of new programs and initiatives, as well as innovations in business models and product design, have emerged in recent years with the aim of making solar more accessible and affordable to broader segments of the population. Yet, many of those efforts are proceeding without robust underlying information about the income characteristics of recent residential PV adopters. This work aims to establish basic factual information about income trends among U.S. residential solar adopters, with some emphasis on low- and moderate-income (LMI) households. The analysis is unique in its relatively extensive coverage of the U.S. solar market, relying on Berkeley Lab’s Tracking the Sun dataset, which contains project-level data for the vast majority of all residential PV systems in the country (a subset of which are ultimately included in the analysis sample). This analysis is also unique in its use of household-level income estimates that provide a more-precise characterization of PV-adopter incomes than in most prior studies.

14 SOLAR ENERGY↗
DOE OSTI2018

Residential Electricity Prices: A Review of Data Sources and Estimation Methods

This paper presents an empirical study of residential electricity prices based on a compilation of tariff data collected for Lawrence Berkeley Lab's Tariff Analysis Project (TAP). We also review other data sources commonly used to estimate residential sector electricity prices, including utility data published by the Energy Information Administration (EIA), the Typical Bills reports published by the Edison Electric Institute, and household-level electricity consumption and expenditure data included with the EIA's Residential Energy Consumption Surveys. We define three different types of price that can be estimated from the data, construct estimation methods, compare the results and evaluate the relative strengths and weaknesses of each approach. We examine several sources of variation including seasonal, regional, industry structure, and the magnitude of baseline household consumption. The tariff data are also used to explore the impact of moving households from standard tariffs to time-of-use pricing, and to summarize how this affects predictions of electricity bill savings for both demand reduction and demand shifting. (auth)

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

The Future of Transportation Electrification: Utility, Industry and Consumer Perspectives

Electric vehicles (EVs) today are a small part of the U.S. transportation fleet. Technological advancements, automotive industry investments and state policies are driving increased transportation electrification. Bloomberg New Energy Finance projects that by 2040, 55 percent of new sales of automobiles worldwide will be EVs. Increased transportation demand for electricity will require additional investments in the distribution system and will impact the bulk power system as load profiles change. At the same time, managed EV charging and discharging can make more efficient use of distribution system assets and increase grid flexibility. EVs also hold promise for lowering transportation costs and reducing air emissions. Infrastructure needs to electrify transportation across the United States far exceed current investment plans by EV charging companies, the public sector and others. Utilities are building “make-ready” infrastructure to ease development of public charging stations and offering rates tailored for EVs, and some utilities are directly investing in charging stations. The growth of EVs raises a number of questions for policymakers and others: How much public charging infrastructure will be needed, where should it be built and when will it be used? What role should utilities play in developing the infrastructure, compared to EV charging companies?How should charging infrastructure costs be allocated among utility customers? How should electricity rates be set to encourage efficient grid use and minimize negative grid impacts? How are states preparing for increasing electrification of the transportation sector? This report in the Future Electric Utility Regulation series from Berkeley Lab, tackles these questions and more. The report approaches the issues from three perspectives: utilities, the EV charging industry and consumers.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Tracking the Sun: Installed Price Trends for Distributed Photovoltaic Systems in the United States - 2018 Edition

Berkeley Lab’s Tracking the Sun report series is dedicated to summarizing installed prices and other trends among grid-connected, distributed solar photovoltaic (PV) systems in the United States. The present report, the 11th edition in the series, focuses on systems installed through year-end 2017, with preliminary trends for the first half of 2018. As in years past, the primary emphasis is on describing changes in installed prices over time and variation in pricing across projects based on location, project ownership, system design, and other attributes. New to this year, however, is an expanded discussion of other project characteristics in the large underlying data sample. Future editions will include more of such material, beyond the report’s traditional focus on installed pricing. The trends described in this report derive primarily from project-level data reported to state agencies and utilities that administer PV incentive programs, solar renewable energy credit (SREC) registration systems, or interconnection processes. In total, data were collected and cleaned for more than 1.3 million individual PV systems, representing 81% of U.S. residential and non-residential PV systems installed through 2017. The analysis of installed pricing trends is based on a subset of roughly 770,000 systems with available installed price data. Key findings from this year’s report include the following: -Installed Prices Continued to Fall through 2017 and into 2018. National median installed prices in 2017 were $3.7 per watt (W) for residential systems (a $0.2/W or 6% decline from the prior year), $3.1/W for small non-residential systems (a $0.4/W or 11% decline), and $2.2/W (a $0.1/W or 5% decline) for large non-residential systems. Data for the first half of 2018 show an additional drop of $0.1/W for residential and small non-residential systems, and effectively no change for large non-residential systems. These recent trends are generally consistent with the pace of price declines since 2014, and mark a slowing from the years immediately preceding (2009-2013) when prices fell by roughly $1/W per year. -Installed Prices Vary Widely Across Individual Projects. Among residential systems installed in 2017, 20% were priced below $3.0/W (the 20th percentile value) while another 20% were above the 80th percentile at $4.5/W. Non-residential systems also exhibit wide spreads, albeit shifted downward, from $2.4/W to $4.1/W for small non-residential and from $1.8/W to $2.8/W for large non-residential projects. These pricing spreads have persisted over time and are both reflective of the heterogeneity in PV systems and markets, as well as suggestive of the potential for further price declines. -Pronounced Pricing Disparities Exist Across States. State-level median installed prices in 2017 ranged from $2.6/W to $4.5/W for residential systems, from $2.2/W to $4.0/W for small non-residential systems, and from $2.1/W to $2.4/W for large non-residential systems. Three of the largest state markets (California, Massachusetts, and New York) are relatively high-priced, pulling overall U.S. median prices upward. These cross-state pricing differences reflect both idiosyncratic features of particular states as well as more-fundamental differences related to market and policy conditions. -Installed Pricing Variation Also Reflects Differences in System Design, Installer, and Customer Characteristics (among other factors). Significant pricing differences were also observed across system sizes within each customer segment (with median prices ranging from $3.2/W to $4.5/W across residential systems of varying size, and from $2.1/W to $3.7/W across non-residential systems), between third-party owned (TPO) and host-owned residential systems ($3.3/W vs. $3.8/W), across residential installers ($2.1/W to $9.6/W for host-owned systems and $1.1/W to $5.1/W for TPO systems), between residential new construction and retrofit systems in California ($2.3/W vs. $3.9/W), between large non-residential systems at commercial sites and tax-exempt customer sites ($2.1/W vs. $2.6/W), and between systems with standard vs. premium efficiency modules ($3.6/W vs. $4.2/W among residential systems). A public version of the underlying dataset can be downloaded at https://emp.lbl.gov/tracking-the-sun.

14 SOLAR ENERGY↗
DOE OSTI2018

The Future of U.S. Electricity Efficiency Programs Funded by Utility Customers: Program Spending and Savings Projections to 2030

Electric utility customers spent about $5.8 billion on energy efficiency programs in 2016 to cost-effectively offset a portion of growth in U.S. power needs. That, in turn, affects the need for investment in new electricity infrastructure, across generation, transmission and distribution systems. A new study by Berkeley Lab provides a bottom-up assessment of the potential impact of existing and likely state policies and market conditions to promote or constrain future spending and savings for electricity efficiency programs funded by utility customers in all U.S. states.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Non-residential Electricity Prices: A Review of Data Sources and Estimation Methods

This paper presents a review of electricity price data sources and estimation methods for non-residential consumers, including a detailed empirical study of tariff-based prices collected as part of Lawrence Berkeley Lab's Tariff Analysis Project (TAP). We compare the TAP data to two other data sources: utility-level annual sales, revenues and consumer counts published by the Energy Information Administration (EIA), and the Typical Bills reports published by the Edison Electric Institute. A brief overview of the types of rate structures included in the TAP database illustrates the often complex relationship between the consumer's energy use and the costs they see on their electricity bills. We provide mathematical definitions of the different types of electricity price that are useful in cost-benefit analysis, construct estimation methods appropriate to each data set, compare the results, and evaluate the relative strengths and weaknesses of each approach. We also examine how price variation is driven by differences in season, region, industry business model, and variables characterizing consumer energy use. Tables of regional price estimates for each data source, based on 2015 data, are included in the Appendix to this paper.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Peak Demand Impacts From Electricity Efficiency Programs

Quantification of the costs and benefits of electricity efficiency programs have focused largely on the economic value of annual energy reductions. State public utility commissions PUCs and utilities are increasingly interested in assessing peak demand impacts of these programs. The U.S. Energy Information Administration defines peak demand as "The maximum load during a specified period of time." In practice, utilities and grid operators use a wide range of definitions for peak demand. With increasing need for a more flexible and resilient electricity system, and changing costs for generation, utilities and other efficiency program administrators must take into account all characteristics of efficiency programs — including peak demand reduction — to ensure a reliable system at the most affordable cost. In this study, Berkeley Lab explored a new metric, the program administrator (PA) cost of saving peak demand (PA CSPD). We collected data on costs, energy savings and peak demand savings for electricity efficiency programs for 36 investor-owned utilities and other PAs in nine states (Arizona, Arkansas, California, Colorado, Illinois, Massachusetts, Maryland, New York and Texas) for 2014 to 2017. While some utilities report demand reductions for their electricity efficiency programs, there has been little sustained effort in the United States to gather peak demand reduction data or benchmark the cost of achieving peak demand impacts. As a first of its kind analysis, we developed a framework for analyzing peak demand savings across utilities and states and begin to quantify the cost of saving peak demand.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Cost of Saving Electricity Through Efficiency Programs Funded by Customers of Publicly Owned Utilities: 2012–2017

This report finds that energy efficiency programs for customers of publicly owned utilities saved electricity at an average cost of 2.4 cents per kilowatt-hour (kWh) from 2012 to 2017. Utilities use such cost performance metrics to assess effectiveness of efficiency program portfolios, determine what programs to offer customers, and, more broadly, ensure electricity system reliability at the most affordable cost as part of electric utility resource adequacy planning and resource procurement processes. The study analyzed efficiency program data reported by 111 program administrators for 219 publicly owned utilities in 14 states — about 90 percent of the municipal utilities and public utility districts that report the data to the Energy Information Administration (EIA). The data represent 88 percent of all spending and 75 percent of all savings that publicly owned utilities reported to EIA in those years. Berkeley Lab used data from several sources, including data provided directly by American Public Power Association members, publicly available annual reports and regional data collections.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

The use of wholesale market purchases by Western U.S. electric utilities

Load Serving Entities (LSEs) can procure electricity from their owned generation or purchase it on bilateral or centrally organized markets. Electric utility procurement decisions have important implications for resource adequacy, which is gaining attention especially in the Western U.S. However, there is little or no research on trends of utility procurement practices, and the role that wholesale market transactions can have for resource adequacy and other applications. This paper leverages information from the U.S. Department of Energy’s Energy Information Administration, Berkeley Lab’s Resource Planning Portal, and structured interviews with regulatory and utility staff. We find that use of market transactions to meet electric energy needs has been increasing across U.S. LSEs. The reasons for this trend include a general stagnation in building owned resources, a preference towards contracting renewable energy resources to meet RPS targets, and the lower relative cost of wholesale market electricity. Among market transactions across different time scales, short-term transactions were prevalent in Western U.S. LSEs until the 2008 recession, and thereafter declined to about a quarter of total purchases. In contrast, long-term market transactions have become more prevalent and resource planning reports project the share of these transactions in the procurement mix may continue to increase. Results suggest resource adequacy obligations may drive preferences for asset ownership more than a profit incentive. We also recommend improving the planning to procurement connection by reflecting in planning assumptions the operational constraints of market transactions. These results provide insights on key issues in electric utility procurement that could improve overall market efficiency, respond to rapidly changing industry conditions, and identify practices that LSEs and regulators should consider incorporating into their long-term transmission and capacity planning processes.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Cost of saving natural gas through efficiency programs funded by utility customers: 2012–2017

This study estimates the cost of saving a therm of natural gas from energy efficiency programs funded by utility customers during the period 2012 to 2017. Berkeley Lab researchers compiled and analyzed efficiency program data reported by investor-owned utilities and other program administrators in a dozen states representative of the four U.S. Census regions — Arkansas, California, Connecticut, Iowa, Massachusetts, Michigan, Minnesota, New Jersey, New York, Oklahoma, Rhode Island and Utah. Depending on the year, the dataset accounts for about 50 percent to 70 percent of annual national spending on natural gas efficiency programs. The estimated cost of saving natural gas during the study period is $0.40 per therm. The analysis also includes estimates of the program administrator cost of saved energy for three core sectors for natural gas: commercial and industrial, residential, and low-income households. It aggregates these sectors to provide regional and national values. Our metrics include savings-weighted averages, unweighted medians, and interquartile ranges (25th and 75th percentiles) of the levelized program administrator cost of saving gas, in constant 2017 dollars. In addition, the study analyzes cost trends during the study period, finding that average program costs trended downward. The U.S. Department of Energy’s Building Technologies Office supported this work.

03 NATURAL GAS↗