Assessment of Energy Demand Management Strategies in Agriculture

As farms have become more sophisticated and automated, the electrical demands of many farms have increased, requiring enhanced needs for high quality electric to power equipment. In 2014, the agricultural sector consumed 1,714 trillion BTU of energy with electricity, representing 17 percent of the total energy consumed in agriculture. Energy inputs are important to agriculture, as electricity costs an average of 1 percent to 6 percent of total expenses for farm businesses. In 2011, about three-fourths of U.S. farms had a profit margin of less than 10 percent, including roughly 61 percent with operating a profit margin of less than 0 percent. Higher energy expenses increase production costs, raise the prices of agricultural products, and reduce farm income. Unlike residential accounts, which are based only on total energy usage, commercial accounts are charged for total energy usage and the peak amount of energy, called demand, used more than a short time period. On some farms, the resulting demand charges can be nearly 50 percent of the farm's monthly electricity bill. While demand charges are often significant, few consumers understand the costs, how they are calculated, and what impact their electrical usage has on their billing. The objective of the agricultural energy management program was to install advanced energy metering equipment in agricultural facilities to track electric demand profiles and monitor power quality. We collected energy usage data for individual motor loads on six farms, allowing our team to analyze how specific operations contribute to the farms overall peak demand charges. Using the detailed energy data from the test facilities, a team from Ohio State's Electrical and Computer Engineering Department developed energy models to simulate load shifting and evaluate the economic impact. The models were validated by comparing the simulation results with data collected from facility measurements. Understanding peak demand charges and energy management strategies in agriculture is a complex issue. As a result, our project partners were strategically designed around four critical disciplines including energy, swine production, dairy production, and electrical engineering. In total, more than 29 project partners contributed to the project including Extension professionals; swine and dairy farmers; the Ohio State College of Food, Agricultural, and Environmental Sciences; the Ohio Agricultural Research and Development Center; and faculty and students in the Ohio State College of Computer and Electrical Engineering. The intended audience for this session includes Extension personnel working with agricultural producers, students, and researchers with interest in energy management and electrical and computer engineering. This session will provide an overview of the project partnerships, research methods, outreach goals, preliminary results, and discuss potential energy management strategies to minimize costs and foster long-term sustainability.AUTHOR AFFILIATION: Eric Romich, OSU Extension field specialist, energy development, [email protected] (Corresponding Author); Mahesh Illindala, associate professor, Ohio State College of Engineering, Electrical and Computer Engineering; Chris Zoller, OSU Extension educator, agriculture and natural resources; Tim Barnes, OSU Extension educator, agriculture and natural resources; Rory Lewandowski, OSU Extension educator, agriculture and natural resourcesThe objective of the agricultural energy management program was to install advanced energy metering equipment in agricultural facilities to track electric demand profiles and monitor power quality. Specifically, we partnered with six farms to collect energy usage data for individual motor loads, allowing our team to analyze how specific operations contribute to each farm's overall peak demand charges. Ohio State's Department of Electrical and Computer Engineering developed energy models to simulate load shifting and evaluate the economic impact. The intended audience includes Extension personnel working with agricultural producers, students, and researchers with interest in energy management and electrical and computer engineering. We will provide an overview of the project partnerships, research methods, outreach and education goals, and preliminary results; and we will discuss potential energy management strategies

Assessment of Energy Demand Management Strategies in Agriculture

As farms have become more sophisticated and automated, the electrical demands of many farms have increased, requiring enhanced needs for high quality electric to power equipment. In 2014, the agricultural sector consumed 1,714 trillion BTU of energy with electricity, representing 17 percent of the total energy consumed in agriculture. Energy inputs are important to agriculture, as electricity costs an average of 1 percent to 6 percent of total expenses for farm businesses. In 2011, about three-fourths of U.S. farms had a profit margin of less than 10 percent, including roughly 61 percent with operating a profit margin of less than 0 percent. Higher energy expenses increase production costs, raise the prices of agricultural products, and reduce farm income. Unlike residential accounts, which are based only on total energy usage, commercial accounts are charged for total energy usage and the peak amount of energy, called demand, used more than a short time period. On some farms, the resulting demand charges can be nearly 50 percent of the farm's monthly electricity bill. While demand charges are often significant, few consumers understand the costs, how they are calculated, and what impact their electrical usage has on their billing. The objective of the agricultural energy management program was to install advanced energy metering equipment in agricultural facilities to track electric demand profiles and monitor power quality. We collected energy usage data for individual motor loads on six farms, allowing our team to analyze how specific operations contribute to the farms overall peak demand charges. Using the detailed energy data from the test facilities, a team from Ohio State's Electrical and Computer Engineering Department developed energy models to simulate load shifting and evaluate the economic impact. The models were validated by comparing the simulation results with data collected from facility measurements. Understanding peak demand charges and energy management strategies in agriculture is a complex issue. As a result, our project partners were strategically designed around four critical disciplines including energy, swine production, dairy production, and electrical engineering. In total, more than 29 project partners contributed to the project including Extension professionals; swine and dairy farmers; the Ohio State College of Food, Agricultural, and Environmental Sciences; the Ohio Agricultural Research and Development Center; and faculty and students in the Ohio State College of Computer and Electrical Engineering. The intended audience for this session includes Extension personnel working with agricultural producers, students, and researchers with interest in energy management and electrical and computer engineering. This session will provide an overview of the project partnerships, research methods, outreach goals, preliminary results, and discuss potential energy management strategies to minimize costs and foster long-term sustainability.AUTHOR AFFILIATION: Eric Romich, OSU Extension field specialist, energy development, [email protected] (Corresponding Author); Mahesh Illindala, associate professor, Ohio State College of Engineering, Electrical and Computer Engineering; Chris Zoller, OSU Extension educator, agriculture and natural resources; Tim Barnes, OSU Extension educator, agriculture and natural resources; Rory Lewandowski, OSU Extension educator, agriculture and natural resourcesThe objective of the agricultural energy management program was to install advanced energy metering equipment in agricultural facilities to track electric demand profiles and monitor power quality. Specifically, we partnered with six farms to collect energy usage data for individual motor loads, allowing our team to analyze how specific operations contribute to each farm's overall peak demand charges. Ohio State's Department of Electrical and Computer Engineering developed energy models to simulate load shifting and evaluate the economic impact. The intended audience includes Extension personnel working with agricultural producers, students, and researchers with interest in energy management and electrical and computer engineering. We will provide an overview of the project partnerships, research methods, outreach and education goals, and preliminary results; and we will discuss potential energy management strategies

Spatial and temporal metagenomics of river compartments reveals viral community dynamics in an urban impacted stream

Although river ecosystems constitute a small fraction of Earth’s total area, they are critical modulators of microbially and virally orchestrated global biogeochemical cycles. However, most studies either use data that is not spatially resolved or is collected at timepoints that do not reflect the short life cycles of microorganisms. To address this gap, we assessed how viral and microbial communities change over a 48-hour period by sampling surface water and pore water compartments of the wastewater-impacted River Erpe in Germany. We sampled every 3 hours resulting in 32 samples for which we obtained metagenomes along with geochemical and metabolite measurements. From our metagenomes, we identified 6,500 viral and 1,033 microbial metagenome assembled genomes (MAGs) and found distinct community membership and abundance associated with each river compartment (e.g., Competibacteraceae in surfacewater and Sulfurimonadaceae in pore water). We show that 17% of our viral MAGs clustered to viruses from other ecosystems like wastewater treatment plants and rivers. Our results also indicated that 70% of the viral community was persistent in surface waters, whereas only 13% were persistent in the pore waters taken from the hyporheic zone. Finally, we predicted linkages between 73 viral genomes and 38 microbial genomes. These putatively linked hosts included members of the Competibacteraceae, which we suggest are potential contributors to river carbon and nitrogen cycling via denitrification and nitrogen fixation. Together, these findings demonstrate that members of the surface water microbiome from this urban river are stable over multiple diurnal cycles. These temporal insights raise important considerations for ecosystem models attempting to constrain dynamics of river biogeochemical cycles

Protocol and quality assurance for carotid imaging in 100,000 participants of UK Biobank: development and assessment.

Background Ultrasound imaging is able to quantify carotid arterial wall structure for the assessment of cerebral and cardiovascular disease risks. We describe a protocol and quality assurance process to enable carotid imaging at large scale that has been developed for the UK Biobank Imaging Enhancement Study of 100,000 individuals. Design An imaging protocol was developed to allow measurement of carotid intima-media thickness from the far wall of both common carotid arteries. Six quality assurance criteria were defined and a web-based interface (Intelligent Ultrasound) was developed to facilitate rapid assessment of images against each criterion. Results and conclusions Excellent inter and intra-observer agreements were obtained for image quality evaluations on a test dataset from 100 individuals. The image quality criteria then were applied in the UK Biobank Imaging Enhancement Study. Data from 2560 participants were evaluated. Feedback of results to the imaging team led to improvement in quality assurance, with quality assurance failures falling from 16.2% in the first two-month period examined to 6.4% in the last. Eighty per cent had all carotid intima-media thickness images graded as of acceptable quality, with at least one image acceptable for 98% of participants. Carotid intima-media thickness measures showed expected associations with increasing age and gender. Carotid imaging can be performed consistently, with semi-automated quality assurance of all scans, in a limited timeframe within a large scale multimodality imaging assessment. Routine feedback of quality control metrics to operators can improve the quality of the data collection