This report describes the implementation and results of a field demonstration wherein residential electric water heaters and thermostats, commercial building space conditioning, municipal water pump loads, and several distributed generators were coordinated to manage constrained feeder electrical distribution through the two-way communication of load status and electric price signals. The field demonstration took place in Washington and Oregon and was paid for by the U.S. Department of Energy and several northwest utilities. Price is found to be an effective control signal for managing transmission or distribution congestion. Real-time signals at 5-minute intervals are shown to shift controlled load in time. The behaviors of customers and their responses under fixed, time-of-use, and real-time price contracts are compared. Peak loads are effectively reduced on the experimental feeder. A novel application of portfolio theory is applied to the selection of an optimal mix of customer contract types. v Executive SummaryPacific Northwest National Laboratory (PNNL) led a field demonstration of smart grid technologies for the U.S. Department of Energy (DOE) and the Pacific Northwest GridWise™ Testbed. The latter is a group composed of several northwest regional utilities, the Bonneville Power Administration (BPA), and PNNL. The overall field demonstration was known as the Pacific Northwest GridWise Testbed Demonstration, composed of two principal projects. This report describes one of these, the Olympic Peninsula Project. The second project, called the Grid Friendly™ Appliance Project, is discussed separately in a companion report. Purpose and ObjectivesThe purpose of the Olympic Peninsula Project was to create and observe a futuristic energy-pricing experiment that illustrates several values of grid transformation that align with the GridWise concept. The central principle of the GridWise concept is that inserting intelligence into electric-grid components at the end-use, distribution, transmission and generation levels will significantly improve both the electrical and economic efficiencies within the electric power system. Specifically, this project, tested whether automated two-way communication between the grid and distributed resources will enable resources to be dispatched based on the energy and demand price signals that they receive. In this manner, conventionally passive loads and idle distributed generators can be transformed into elements of a diverse system of grid resources that provide near real-time active grid control and a broad range of economic benefits. Foremost, the project controlled these resources to successfully manage the power flowing through a constrained feeder-distribution circuit for the duration of the project. In other words, the project tested whether it was possible to decrease the stress on the distribution system at times of peak demand by more actively engaging typically passive resources-end use loads and idle distributed generation.The immediate objectives of the project...
We consider the problem of model selection for geospatial data. Spatial correlation is often ignored in the selection of explanatory variables, and this can influence model selection results. For example, the importance of particular explanatory variables may not be apparent when spatial correlation is ignored. To address this problem, we consider the Akaike Information Criterion (AIC) as applied to a geostatistical model. We offer a heuristic derivation of the AIC in this context and provide simulation results that show that using AIC for a geostatistical model is superior to the often-used traditional approach of ignoring spatial correlation in the selection of explanatory variables. These ideas are further demonstrated via a model for lizard abundance. We also apply the principle of minimum description length (MDL) to variable selection for the geostatistical model. The effect of sampling design on the selection of explanatory covariates is also explored. R software to implement the geostatistical model selection methods described in this paper is available in the Supplement.
A combined mid-infrared spectroscopic/statistical modeling approach for the discrimination and identification, at the strain level, of both sporulated and vegetative bacterial samples is presented. Transmission mode spectra of bacteria dried on ZnSe windows were collected using a Fourier transform mid-infrared (FT-IR) spectrometer. Five Bacillus bacterial strains (B. atrophaeus 49337, B. globigii Dugway, B. thuringiensis spp. kurstaki 35866, B. subtilis 49760, and B. subtilis 6051) were used to construct a reference spectral library and to parameterize a four-step statistical model for the systematic identification of bacteria. The statistical methods used in this initial feasibility study included principal component analysis (PCA), classification and regression trees (CART), and Mahalanobis distance calculations. Internal cross-validation studies successfully classified 100% of the samples into their correct physiological state (sporulated or vegetative) and identified 67% of the samples correctly as to their bacterial strain. Analysis of thirteen blind samples, which included reference and other bacteria, nonbiological materials, and mixtures of both nonbiological and bacterial samples, yielded comparable accuracy. The primary advantage of this approach is the accurate identification of unknown bacteria, including spores, in a matter of minutes.
A classification scheme is created to map the synoptic-scale (large scale) atmospheric state to distributions of local-scale cloud properties. This mapping is accomplished by a neural network that classifies 17 months of synoptic-scale initial conditions from the rapid update cycle forecast model into 25 different states. The corresponding data from a vertically pointing millimeter-wavelength cloud radar (from the Atmospheric Radiation Measurement Program Southern Great Plains site at Lamont, Oklahoma) are sorted into these 25 states, producing vertical profiles of cloud occurrence. The temporal stability and distinctiveness of these 25 profiles are analyzed using a bootstrap resampling technique. A stable-state-based mapping from synoptic-scale model fields to local-scale cloud properties could be useful in three ways. First, such a mapping may improve the understanding of differences in cloud properties between output from global climate models and observations by providing a physical context. Second, this mapping could be used to identify the cause of errors in the modeled distribution of clouds—whether the cause is a difference in state occurrence (the type of synoptic activity) or the misrepresentation of clouds for a particular state. Third, robust mappings could form the basis of a new statistical cloud parameterization.
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