Economic Analysis of an Integrated Wind-Hydrogen Energy System for a Small Alaska Community

Wind-hydrogen systems provide one way to store intermittent wind energy as hydrogen. We explored the hypothesis that an integrated wind-hydrogen system supplying electricity, heat, and transportation fuel could serve the needs of an isolated (off-grid) Alaska community at a lower cost than a collection of separate systems. Analysis indicates that: 1) Combustible Hydrogen could be produced with current technologies for direct use as a transportation fuel for about $15/gallon-equivalent; 2) The capital cost of the wind energy rather than the capital cost of electrolyzers dominates this high cost; and 3) There do not appear to be diseconomies of small scale for current electrolyzers serving a a village of 400 people.United States Department of Energy. DOE Award Number: DE-FC26-01NT41248Introduction / Executive Summary / Experimental Methods / Results and Discussion / Conclusion / Bibliography / Appendix: Associated Excel Workbook

Hysterectomy, endometrial ablation, and levonorgestrel releasing intrauterine system (Mirena) for treatment of heavy menstrual bleeding : cost effectiveness analysis

Peer reviewedPublisher PD

Cooperative co-evolution of GA-based classifiers based on input increments

Genetic algorithms (GAs) have been widely used as soft computing techniques in various applications, while cooperative co-evolution algorithms were proposed in the literature to improve the performance of basic GAs. In this paper, a new cooperative co-evolution algorithm, namely ECCGA, is proposed in the application domain of pattern classification. Concurrent local and global evolution and conclusive global evolution are proposed to improve further the classification performance. Different approaches of ECCGA are evaluated on benchmark classification data sets, and the results show that ECCGA can achieve better performance than the cooperative co-evolution genetic algorithm and normal GA. Some analysis and discussions on ECCGA and possible improvement are also presented

Benchmarking Utility Clean Energy Deployment: 2016

Benchmarking Utility Clean Energy Deployment: 2016 provides a window into how the global transition toward clean energy is playing out in the U.S. electric power sector. Specifically, it reveals the extent to which 30 of the largest U.S. investor-owned electric utility holding companies are increasingly deploying clean energy resources to meet customer needs.Benchmarking these companies provides an opportunity for transparent reporting and analysis of important industry trends. It fills a knowledge gap by offering utilities, regulators, investors, policymakers and other stakeholders consistent and comparable information on which to base their decisions. And it provides perspective on which utilities are best positioned in a shifting policy landscape, including likely implementation of the U.S. EPA's Clean Power Plan aimed at reducing carbon pollution from power plants

Benchmarking Utility Clean Energy Deployment: 2014

This report assembles data from more than 10 sources, including state Renewable Portfolio Standard (RPS) annual reports, U.S. Securities and Exchange Commission 10-K filings and Public Utility Commission reports, to show how 32 of the largest U.S. investor-owned electric utility holding companies stack up on renewable energy and energy efficiency

Long-term Framework for Electricity Distribution Access Charges

In order to achieve overall economic efficiency, incentive regulation of electricity distribution utilities must address two important and inter-related issues. First, the utilities’ allowed revenues need to be set at correct levels. Second, the access charging mechanism by which the utilities recover the allowed revenues must give the correct economic signals to generation and load connected to the network. This paper is concerned with the latter aspect of regulation. The paper discusses the main economic principles that should form the basis on which a distribution access charging model is developed. The charging model should have a number of attributes: be calibrated to each existing network; contain an asset register; be able to determine assets needed to meet new demand; find least-cost system expansion; compute network losses and handle ancillary services; estimate incremental operating and maintenance costs; be available to users; and be simple enough for external users to understand

Task Runtime Prediction in Scientific Workflows Using an Online Incremental Learning Approach

Many algorithms in workflow scheduling and resource provisioning rely on the performance estimation of tasks to produce a scheduling plan. A profiler that is capable of modeling the execution of tasks and predicting their runtime accurately, therefore, becomes an essential part of any Workflow Management System (WMS). With the emergence of multi-tenant Workflow as a Service (WaaS) platforms that use clouds for deploying scientific workflows, task runtime prediction becomes more challenging because it requires the processing of a significant amount of data in a near real-time scenario while dealing with the performance variability of cloud resources. Hence, relying on methods such as profiling tasks' execution data using basic statistical description (e.g., mean, standard deviation) or batch offline regression techniques to estimate the runtime may not be suitable for such environments. In this paper, we propose an online incremental learning approach to predict the runtime of tasks in scientific workflows in clouds. To improve the performance of the predictions, we harness fine-grained resources monitoring data in the form of time-series records of CPU utilization, memory usage, and I/O activities that are reflecting the unique characteristics of a task's execution. We compare our solution to a state-of-the-art approach that exploits the resources monitoring data based on regression machine learning technique. From our experiments, the proposed strategy improves the performance, in terms of the error, up to 29.89%, compared to the state-of-the-art solutions.Comment: Accepted for presentation at main conference track of 11th IEEE/ACM International Conference on Utility and Cloud Computin

Automatically Leveraging MapReduce Frameworks for Data-Intensive Applications

MapReduce is a popular programming paradigm for developing large-scale, data-intensive computation. Many frameworks that implement this paradigm have recently been developed. To leverage these frameworks, however, developers must become familiar with their APIs and rewrite existing code. Casper is a new tool that automatically translates sequential Java programs into the MapReduce paradigm. Casper identifies potential code fragments to rewrite and translates them in two steps: (1) Casper uses program synthesis to search for a program summary (i.e., a functional specification) of each code fragment. The summary is expressed using a high-level intermediate language resembling the MapReduce paradigm and verified to be semantically equivalent to the original using a theorem prover. (2) Casper generates executable code from the summary, using either the Hadoop, Spark, or Flink API. We evaluated Casper by automatically converting real-world, sequential Java benchmarks to MapReduce. The resulting benchmarks perform up to 48.2x faster compared to the original.Comment: 12 pages, additional 4 pages of references and appendi