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

Long-Term Energy Solutions: The Truth Behind the Silent Lie

Edge computingZivkovic, Miodrag Bezdan, Timea Strumberger, Ivana Bacanin, Nebojsa Venkatachalam, K. is a relatively novel technology, which is closely related to the concepts of the Internet of things and cloud computing. The main purpose of edge computing is to bring the resources as close as possible to the clients, to the very edge of the cloud. By doing so, it is possible to achieve smaller response times and lower network bandwidth utilization. Workflow scheduling in such an edge–cloud environment is considered to be an NP-hard problem, which has to be solved by a stochastic approach, especially in the scenario of multiple optimization goals. In the research presented in this paper, a modified Harris hawks optimization algorithm is proposed and adjusted to target cloud–edge workflow scheduling problem. Simulations are carried out with two main objectives—cost and makespan. The proposed experiments have used real workflow models and evaluated the proposed algorithm by comparing it to the other approaches available in the recent literature which were tested in the same simulation environment and experimental conditions. Based on the results from conducted experiments, the proposed improved Harris hawks optimization algorithm outperformed other state-of-the-art approaches by reducing cost and makespan performance metrics.

Despite the abundant research in cloud autoscaling, autoscaling in Kubernetes, arguably the most popular cloud platform today, is largely unexplored. Kubernetes' Cluster Autoscaler can be configured to select nodes either from a single node pool (CA) or from multiple node pools (CA-NAP). We evaluate and compare these configurations using two representative applications and workloads on Google Kubernetes Engine (GKE). We report our results using monetary cost and standard autoscaling performance metrics (under- and over-provisioning accuracy, under- and over-provisioning timeshare, instability of elasticity and deviation from the theoretical optimal autoscaler) endorsed by the SPEC Cloud Group. We show that, overall, CA-NAP outperforms CA and that autoscaling performance depends mainly on the composition of the workload. We compare our results with those of the related work and point out further configuration tuning opportunities to improve performance and cost-saving.

The Ernest Orlando Lawrence Berkeley National Laboratory (Berkeley Lab or LBNL) is a multi-program national research facility operated by the University of California for the Department of Energy (DOE). As an integral element of DOE's National Laboratory System, Berkeley Lab supports DOE's missions in fundamental science, energy resources, and environmental quality. Berkeley Lab programs advance four distinct goals for DOE and the nation: (1) To perform leading multidisciplinary research in the computing sciences, physical sciences, energy sciences, biosciences, and general sciences in a manner that ensures employee and public safety and protection of the environment. (2) To develop and operate unique national experimental facilities for qualified investigators. (3) To educate and train future generations of scientists and engineers to promote national science and education goals. (4) To transfer knowledge and technological innovations and to foster productive relationships among Berkeley Lab's research programs, universities, and industry in order to promote national economic competitiveness. Annual report on Laboratory Directed Research and Development for FY2000.

The Ernest Orlando Lawrence Berkeley National Laboratory (Berkeley Lab or LBNL) is a multi-program national research facility operated by the University of California for the Department of Energy (DOE). As an integral element of DOE's National Laboratory System, Berkeley Lab supports DOE's missions in fundamental science, energy resources, and environmental quality. Berkeley Lab programs advance four distinct goals for DOE and the nation: (1) To perform leading multidisciplinary research in the computing sciences, physical sciences, energy sciences, biosciences, and general sciences in a manner that ensures employee and public safety and protection of the environment. (2) To develop and operate unique national experimental facilities for qualified investigators. (3) To educate and train future generations of scientists and engineers to promote national science and education goals. (4) To transfer knowledge and technological innovations and to foster productive relationships among Berkeley Lab's research programs, universities, and industry in order to promote national economic competitiveness.

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 164816, ’Combining Geostatistics With Bayesian Updating To Continually Optimize Drilling Strategy in Shale-Gas Plays,’ by B.J.A. Willigers, BG Group; S. Begg, University of Adelaide; and R.B. Bratvold, University of Stavanger, prepared for the 2013 EAGE Annual Conference and Exhibition incorporating SPE Europec, London, 10-13 June. The paper has not been peer reviewed. The development of an economically efficient drilling program in shale-gas plays is a challenging task, requiring a large number of wells; even with many wells, the average well production and the variation of well performance (economics) remain highly uncertain. The ability to delineate a shale play with the fewest number of wells and to focus drilling in the most productive areas is an important driver of commercial success. Here, we present a new methodology for improving the economic returns of shale-gas plays. Introduction The methodology proposed in this study informs the development of drilling policies for shale-gas opportunities by use of a probabilistic model that accounts for the uncertainty in the chance of success (COS) and its spatial dependency. The model developed in this study shows how a prior view of the COS across a shale-gas play is updated as more wells are drilled. Inferred chances of success of undrilled well locations allow decision makers to react to newly arriving well data and optimize the development of a shale-gas resource. The main contributions of this paper are (1) to establish and quantify any spatial dependencies between the performances of shale-gas wells, an assessment based upon data from the Barnett shale; (2) present a comprehensive method for updating COS probabilities consistently with the arrival of new information in a play with spatial dependencies; and (3) demonstrate the approach through an analysis of data from the Barnett shale. For a discussion of previous work performed in uncertainty resolution in inconventional gas plays, including the role played by spatial dependency, please see the complete paper. Shale-Gas Economics in the Study Area Well Data. The investigated area is located in the eastern part of the Mississippian Barnett shale in the Fort Worth basin in Texas (Fig. 1). The study area covers 2100 km2 and straddles Johnson, Tarrant, and Ellis Counties. A set of 2,901 horizontal wells has been analyzed; 2,064 of these are located in the “Training” area (see later reference in this subsection). Well data include well location; date of completion; first 6-, 12-, or 24-month cumulative gas production; and the length of the well. The data originate from the US Energy Information Administration (www.eia.gov).

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.

Conservation voltage reduction (CVR) will reduce demand on an electric system. More controversial is the idea that CVR can reduce energy usage on a system. Although CVR has its proponents, it is difficult to implement over a wide area without the danger of exposing some customers to unacceptable under voltage conditions. Another issue is that CVR does not cause reduced energy usage for all loads, in fact some loads may increase energy usage when voltage is reduced. Bonneville Power Administration (BPA) has agreed to reimburse several of it's customers who are public utility districts or electric cooperatives for either the cost of installing CVR systems or the energy saved with a CVR system. This manuscript discusses systems installed by two electric utilities in the Pacific Northwest that are being used to implement CVR using new communication and control technologies to prevent under voltage conditions and assure actual reduced energy usage.

This publication, issued annually since 1946, presents financial and operating information about publicly owned electric utilities in the United States. The statistics presented in this publication are compiled from data taken from the annual reports filed by the utilities with the Energy Information Administration on Form ERA-412, Annual Report of Municipal Electric Utilities with Annual Revenues of $250,000 or more.

The SEMATECH High-NA Actinic Reticle review Project (SHARP) is a synchrotron-based, EUV-wavelength microscope, dedicated to photomask imaging, now being commissioned at Lawrence Berkeley National Laboratory. In terms of throughput, resolution, coherence control, stability and ease of use, SHARP represents a significant advance over its predecessor, the SEMATECH Berkeley Actinic Inspection Tool (AIT), which was decommissioned in September 2012. SHARP utilizes several advanced technologies to achieve its design goals: including the first Fouriersynthesis illuminator on a zoneplate microscope, EUV MEMS mirrors, and high-efficiency freestanding zoneplate lenses with numerical aperture values up to 0.625 (4×). In its first week of operation, SHARP demonstrated approximately 150 times higher light throughput than AIT and a spatial resolution down to 55-nm half-pitch with 0.42 4×NA (i.e. the smallest feature size on our test mask.) This paper describes the current status of the tool commissioning and the performance metrics available at this early stage.

The Department of Energy (DOE) Fuel Cells for Transportation Program is structured to effectively implement the research and development (R and D) required for highly efficient, low or zero emission fuel cell power systems to be a viable replacement for the internal combustion engine in automobiles. The Program is part of the Partnership for a New Generation of Vehicles (PNGV), a government-industry initiative aimed at development of an 80 mile-per-gallon vehicle. This Annual Report summarizes the technical accomplishments of the laboratories during 1997. Participants include: Argonne National Laboratory (ANL), Brookhaven National Laboratory (BNL), Lawrence Berkeley National Laboratory (LBNL), Los Alamos National Laboratory (LANL), Oak Ridge National Laboratory (ORNL), Pacific Northwest National Laboratory (PNNL), and the National Renewable Energy Laboratory (NREL). During 1997, the laboratory R and D included one project on solid oxide fuel cells; this project has since been terminated to focus Department resources on PEM fuel cells. The technical component of this report is divided into five key areas: fuel cell stack research and development; fuel processing; fuel cell modeling, testing, and evaluation; direct methanol PEM fuel cells; and solid oxide fuel cells.

This report presents an overview of the environment, safety, and health program in operation at the Lawrence Berkeley Laboratory. description of research in environmental science, remediation, waste management, safety, health services, radiation assessment, and emergency plans are provided.

How to map IP cores onto NoC architectures is a significant issue (application mapping) in multi-core system design. Many mapping algorithms which aim at optimizing cost metrics(e.g. energy consumption) in the mapping procedure are proposed. Some of those algorithms consider satisfying performance metrics (e.g. latency) constraints. This paper analyses the mechanism leading to performance decreases and furthermore proposes a new approach SPC (satisfy processing capacity) to guarantee performance metrics for cost oriented application mapping algorithms. Using this approach in one of those algorithms, performance metrics constraints are well ensured and cost metrics are hardly affected. In fact, one random experiment shows that performance is enhanced at most 10% even if cost is reduced by 6%.

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.

Analysts assessing energy policies and energy modelers forecasting future trends need to have access to reliable and concise energy statistics. Lawrence Berkeley National Laboratory evaluated several sources of California energy data, primarily from the California Energy Commission and the U.S. Energy Information Administration, to develop the California Energy Balance Database (CALEB). This database manages highly disaggregated data on energy supply, transformation, and end-use consumption for each type of energy commodity from 1990 to the most recent year available (generally 2001) in the form of an energy balance, following the methodology used by the International Energy Agency. This report presents the data used for CALEB and provides information on how the various data sources were reconciled. CALEB offers the possibility of displaying all energy flows in numerous ways (e.g.,physical units, Btus, petajoules, different levels of aggregation), facilitating comparisons among the different types of energy commodities and different end-use sectors. In addition to displaying energy data, CALEB can also be used to calculate state-level energy-related carbon dioxide emissions using the methodology of the Intergovernmental Panel on Climate Change.