Increasing Utilization of US Electric Grids via Smart Technologies: Integration of Load Management, Real Time Pricing, Renewables, EVs and Smart Grid Sensors

Electric power grids throughout the world suffer from serious inefficiencies associated with under-utilization due to demand patterns, engineering design and load following approaches in use today. These grids consume much of the world’s energy and represent a large carbon footprint. From material utilization perspectives significant hardware is manufactured and installed for this infrastructure often to be used at less than 20-40% of its operational capacity for most of its lifetime. These inefficiencies lead engineers to require additional grid support and conventional generation capacity additions when renewable technologies (such as solar and wind) and electric vehicles are to be added to the utility demand/supply mix. Using actual data from the PJM [PJM 2009] the work shows that consumer load management, real time price signals, sensors and intelligent demand/supply control offer a compelling path forward to increase the efficient utilization and carbon footprint reduction of the world’s grids. Underutilization factors from many distribution companies indicate that distribution feeders are often operated at only 70-80% of their peak capacity for a few hours per year, and on average are loaded to less than 30-40% of their capability. By creating strong societal connections between consumers and energy providers technology can radically change this situation. Intelligent deployment of smart sensors, smart electric vehicles, consumer-based load management technology very high saturations of intermittent renewable energy supplies can be effectively controlled and dispatched to increase the levels of utilization of existing utility distribution, substation, transmission, and generation equipment. The strengthening of these technology, society and consumer relationships requires rapid dissemination of knowledge (real time prices, costs & benefit sharing, demand response requirements) in order to incentivize behaviors that can increase the effective use of technological equipment that represents one of the largest capital assets modern society has created.

The Economics of Residential Solid Waste Management

This paper provides a broad overview of recent trends in solid waste and recycling, related public policy issues, and the economics literature devoted to these topics. Public attention to solid waste and recycling has increased dramatically over the past decade both in the United States and in Europe. In response, economists have developed models to help policy makers choose the efficient mix of policy levers to regulate solid waste and recycling activities. Economists have also employed different kinds of data to estimate the factors that contribute to the generation of residential solid waste and recycling and to estimate the effectiveness of many of the policy options employed.

New Results Concerning Probability Distributions with Increasing Generalized Failure Rates

The generalized failure rate of a continuous random variable has demonstrable importance in operations management. If the valuation distribution of a product has an increasing generalized failure rate (that is, the distribution is IGFR), then the associated revenue function is unimodal, and when the generalized failure rate is strictly increasing, the global maximum is uniquely specified. The assumption that the distribution is IGFR is thus useful and frequently held in recent pricing, revenue, and supply chain management literature. This note contributes to the IGFR literature in several ways. First, it investigates the prevalence of the IGFR property for the left and right truncations of valuation distributions. Second, we extend the IGFR notion to discrete distributions and contrast it with the continuous distribution case. The note also addresses two errors in the previous IGFR literature. Finally, for future reference, we analyze all common (continuous and discrete) distributions for the prevalence of the IGFR property, and derive and tabulate their generalized failure rates.

Depressive symptoms as a predictor of alcohol relapse after residential treatment programs for alcohol use disorder

Alcohol use disorder (AUD) and depressive disorders often co-occur. Findings on the effects of major depressive disorder (MDD) or depressive symptoms on posttreatment alcohol relapse are controversial. The study's aim is to examine the association of MDD and depressive symptoms with treatment outcomes after residential AUD programs. In a naturalistic-prospective, multisite study with 12 residential AUD treatment programs in the German-speaking part of Switzerland, 64 patients with AUD with MDD, 283 patients with AUD with clinically significant depressive symptoms at admission, and 81 patients with AUD with such problems at discharge were compared with patients with AUD only on alcohol use, depressive symptoms, and treatment service utilization. MDD was provisionally identified at admission and definitively defined at discharge. Whereas patients with MDD did not differ from patients with AUD only at 1-year follow-up, patients with AUD with clinically significant depressive symptoms had significantly shorter time-to-first-drink and a lower abstinence rate. These patients also had elevated AUD indices and treatment service utilization for psychiatric disorders. Our results suggest that clinically significant depressive symptoms are a substantial risk factor for relapse so that it may be important to treat them during and after residential AUD treatment programs.

Meta-analysis identifies 29 additional ulcerative colitis risk loci, increasing the number of confirmed associations to 47

Genome-wide association studies and candidate gene studies in ulcerative colitis have identified 18 susceptibility loci. We conducted a meta-analysis of six ulcerative colitis genome-wide association study datasets, comprising 6,687 cases and 19,718 controls, and followed up the top association signals in 9,628 cases and 12,917 controls. We identified 29 additional risk loci (P < 5 × 10(-8)), increasing the number of ulcerative colitis-associated loci to 47. After annotating associated regions using GRAIL, expression quantitative trait loci data and correlations with non-synonymous SNPs, we identified many candidate genes that provide potentially important insights into disease pathogenesis, including IL1R2, IL8RA-IL8RB, IL7R, IL12B, DAP, PRDM1, JAK2, IRF5, GNA12 and LSP1. The total number of confirmed inflammatory bowel disease risk loci is now 99, including a minimum of 28 shared association signals between Crohn's disease and ulcerative colitis.

Treatment outcomes of an integrated residential programme for patients with schizophrenia and substance use disorder

About half of all schizophrenic patients have a co-occurring substance use disorder, leading to poorer social and functional outcomes than obtained in non-abusing patients. To improve outcomes, integrated treatments have been designed that address the two conditions simultaneously. Results are, however, conflicting because the available effect studies are hampered by various methodological issues, among which are heterogeneous patient samples.

Mapping the increasing risk of human alveolar echinococcosis in Limburg, The Netherlands

The parasite Echinococcus multilocularis was first detected in The Netherlands in 1996 and repeated studies have shown that the parasite subsequently spread in the local population of foxes in the province of Limburg. It was not possible to quantify the human risk of alveolar echinococcosis because no relationship between the amount of parasite eggs in the environment and the probability of infection in humans was known. Here, we used the spread of the parasite in The Netherlands as a predictor, together with recently published historical records of the epidemiology of alveolar echinococcosis in Switzerland, to achieve a relative quantification of the risk. Based on these analyses, the human risk in Limburg was simulated and up to three human cases are predicted by 2018. We conclude that the epidemiology of alveolar echinococcosis in The Netherlands might have changed from a period of negligible risk in the past to a period of increasing risk in the forthcoming years.

A prediction model for assessing residential radon concentration in Switzerland

Indoor radon is regularly measured in Switzerland. However, a nationwide model to predict residential radon levels has not been developed. The aim of this study was to develop a prediction model to assess indoor radon concentrations in Switzerland. The model was based on 44,631 measurements from the nationwide Swiss radon database collected between 1994 and 2004. Of these, 80% randomly selected measurements were used for model development and the remaining 20% for an independent model validation. A multivariable log-linear regression model was fitted and relevant predictors selected according to evidence from the literature, the adjusted R², the Akaike's information criterion (AIC), and the Bayesian information criterion (BIC). The prediction model was evaluated by calculating Spearman rank correlation between measured and predicted values. Additionally, the predicted values were categorised into three categories (50th, 50th-90th and 90th percentile) and compared with measured categories using a weighted Kappa statistic. The most relevant predictors for indoor radon levels were tectonic units and year of construction of the building, followed by soil texture, degree of urbanisation, floor of the building where the measurement was taken and housing type (P-values <0.001 for all). Mean predicted radon values (geometric mean) were 66 Bq/m³ (interquartile range 40-111 Bq/m³) in the lowest exposure category, 126 Bq/m³ (69-215 Bq/m³) in the medium category, and 219 Bq/m³ (108-427 Bq/m³) in the highest category. Spearman correlation between predictions and measurements was 0.45 (95%-CI: 0.44; 0.46) for the development dataset and 0.44 (95%-CI: 0.42; 0.46) for the validation dataset. Kappa coefficients were 0.31 for the development and 0.30 for the validation dataset, respectively. The model explained 20% overall variability (adjusted R²). In conclusion, this residential radon prediction model, based on a large number of measurements, was demonstrated to be robust through validation with an independent dataset. The model is appropriate for predicting radon level exposure of the Swiss population in epidemiological research. Nevertheless, some exposure misclassification and regression to the mean is unavoidable and should be taken into account in future applications of the model.