Economic Sizing of Distributed Energy Resources for Reliable Community Microgrids

Community microgrids offer many advantages for power distribution systems. When there is an extreme event happening, distribution systems can be seamlessly partitioned into several community microgrids for uninterrupted supply to the end-users. In order to guarantee the system reliability, distributed energy resources (DERs) should be sized for ensuring generation adequacy to cover unexpected events. This paper presents a comprehensive methodology for DERs selection in community microgrids, and an economic approach to meet the system reliability requirements. Algorithms of discrete time Fourier transform (DTFT) and particle swarm optimization (PSO) are employed to find the optimal solution. Uncertainties of load demand and renewable generation are taken into consideration. As part of the case study, a sensitivity analysis is carried out to show the renewable generation impact on DERs' capacity planning.Comment: 5 pages, 6 figures, 1 table, 2017 IEEE Power & Energy Society General Meeting. arXiv admin note: substantial text overlap with arXiv:1708.0102

A Rodential Reckoning: A Case Report and Systematic Review of Streptobacillary Endocarditis

Introduction: Endocarditis is a rare, often fatal complication of rat bite fever caused by Streptobacillus moniliformis. Only 39 cases have been reported (including this case) as of 2022. We describe a case and aim to perform this entity’s first systematic literature review. Methods: We performed a systematic review in CENTRAL, EMBASE, MEDLINE, SciELO, and LILACS. The terms used were terms used were (but not limited to) rat bite fever, Streptobacillus moniliformis, Spirillum minus, and endocarditis. We included all abstracts and articles with patients with echocardiographic or histologicproven endocarditis. In case of discordance, a third reviewer was involved. Our protocol was submitted to PROSPERO (CRD42022334092). We also performed searches for studies on the reference list of included articles. Results: We retrieved 108 and included 36 abstracts and articles. A total of 39 patients (including our report) were identified. The mean age was 41.27, and 61.5% were males. The most common findings were fever, murmur, arthralgias, fatigue, splenomegaly, and rash. Underlying heart disease was present in 33%. Exposure to rats was noted in 71.8% of patients, with 56.4% recalling a rat bite. Anemia was seen in 57%, leukocytosis in 52%, and elevated inflammatory markers in 58% that had lab work performed. The mitral valve was most affected, followed by the aortic, tricuspid, and pulmonary valves. Surgical intervention was required in 14 (36%) cases. Of those, 10 required valve replacement. Death was reported in 36% of cases. Unfortunately, the literature available is limited to case series and reports. Conclusion: Our review allows clinicians to suspect better, diagnose, and manage Streptobacillary endocarditis

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