Sue Ellen Haupt

A Wind Power Forecasting System to Optimize Grid Integration

Wind power forecasting can enhance the value of wind energy by improving the reliability of integrating this variable resource and improving the economic feasibility. The National Center for Atmospheric Research (NCAR) has collaborated with Xcel Energy to develop a multifaceted wind power prediction system. Both the day-ahead forecast that is used in trading and the short-term forecast are critical to economic decision making. This wind power forecasting system includes high resolution and ensemble modeling capabilities, data assimilation, now-casting, and statistical postprocessing technologies. The system utilizes publicly available model data and observations as well as wind forecasts produced from an NCAR-developed deterministic mesoscale wind forecast model with real-time four-dimensional data assimilation and a 30-member model ensemble system, which is calibrated using an Analogue Ensemble Kalman Filter and Quantile Regression. The model forecast data are combined using NCARs Dynamic Integrated Forecast System (DICast). This system has substantially improved Xcels overall ability to incorporate wind energy into their power mix.

Artificial Intelligence Methods in the Environmental Sciences

How can environmental scientists and engineers use the increasing amount of available data to enhance our understanding of planet Earth, its systems and processes? This book describes various potential approaches based on artificial intelligence (AI) techniques, including neural networks, decision trees, genetic algorithms and fuzzy logic. Part I contains a series of tutorials describing the methods and the important considerations in applying them. In Part II, many practical examples illustrate the power of these techniques on actual environmental problems. International experts bring to life ways to apply AI to problems in the environmental sciences. While one culture entwines ideas with a thread, another links them with a red line. Thus, a red thread ties the book together, weaving a tapestry that pictures the natural data-driven AI methods in the light of the more traditional modeling techniques, and demonstrating the power of these data-based methods.

Source Characterization with a Genetic Algorithm-Coupled Dispersion-Backward Model Incorporating SCIPUFF

Abstract This paper extends the approach of coupling a forward-looking dispersion model with a backward model using a genetic algorithm (GA) by incorporating a more sophisticated dispersion model [the Second-Order Closure Integrated Puff (SCIPUFF) model] into a GA-coupled system. This coupled system is validated with synthetic and field experiment data to demonstrate the potential applicability of the coupled model to emission source characterization. The coupled model incorporating SCIPUFF is first validated with synthetic data produced by SCIPUFF to isolate issues related directly to SCIPUFF’s use in the coupled model. The coupled model is successful in characterizing sources even with a moderate amount of white noise introduced into the data. The similarity to corresponding results from previous studies using a more basic model suggests that the GA’s performance is not sensitive to the dispersion model used. The coupled model is then tested using data from the Dipole Pride 26 field tests to determine i...

An Empirical Model of Barotropic Atmospheric Dynamics and Its Response to Tropical Forcing

Abstract A linear empirical model of barotropic atmospheric dynamics is constructed in which the streamfunction tendency field is optimally predicted using the concurrent streamfunction state as a predictor. The prediction equations are those resulting from performing a linear regression between tendency and state vectors. Based on the formal analogy between this model and the linear nondivergent barotropic vorticity equation, this empirical model is applied to problems normally addressed with a conventional model based on physical principles. It is found to qualitatively represent the horizontal dispersion of energy and to skillfully predict how a general circulation model will respond to steady tropical heat sources. Analysis of model solutions indicates that the empirical model’s dynamics include processes that are not represented by conventional nondivergent linear models. Most significantly, the influence of internally generated midlatitude divergence anomalies and of anomalous vorticity fluxes by hi...

Validation of a Receptor–Dispersion Model Coupled with a Genetic Algorithm Using Synthetic Data

Abstract A methodology for characterizing emission sources is presented that couples a dispersion and transport model with a pollution receptor model. This coupling allows the use of the backward (receptor) model to calibrate the forward (dispersion) model, potentially across a wide range of meteorological conditions. Moreover, by using a receptor model one can calibrate from observations taken in a multisource setting. This approach offers practical advantages over calibrating via single-source artificial release experiments. A genetic algorithm is used to optimize the source calibration factors that couple the two models. The ability of the genetic algorithm to correctly couple these two models is demonstrated for two separate source–receptor configurations using synthetic meteorological and receptor data. The calibration factors underlying the synthetic data are successfully reconstructed by this optimization process. A Monte Carlo technique is used to compute error bounds for the resulting estimates o...

Solar Forecasting: Methods, Challenges, and Performance

The deployment of solar-based electricity generation, especially in the form of photovoltaics (PVs), has increased markedly in recent years due to a wide range of factors including concerns over greenhouse gas emissions, supportive government policies, and lower equipment costs. Still, a number of challenges remain for reliable, efficient integration of solar energy. Chief among them will be developing new tools and practices that manage the variability and uncertainty of solar power.

UAV navigation by an expert system for contaminant mapping with a genetic algorithm

Source characterization for an unknown contaminant release can be achieved by inverting an atmospheric transport and dispersion model given concentration observations from a moderately dense spatial array at one or more times. Achieving the required observation density over large geographic regions can, however, be prohibitively expensive if fixed sensors are employed. Mobile sensors provide a cost-saving alternative, with unmanned aerial vehicles (UAVs) being particularly well suited for these large-area problems because of their relatively high speed. The challenge then becomes to devise a set of navigation rules by which the aircraft can determine the route which most expeditiously acquires the required concentration observations. This task involves physical reasoning based on the wind vector, ongoing concentration observations, and current estimates of source position to plan leg length and direction. Each flight leg is planned based on data from all of the prior legs, the flight plan adapts to the observations as they are taken. Expert system navigation systems are developed for two situations: instantaneous (puff) and continuous (plume) releases. Of the two the puff poses the greater challenge because it provides a moving target rather than a quasi-steady concentration pattern. Thus, this rule-based navigation system must guide the UAV to an intercept for each pass through the puff rather than just sweeping across the contaminant field at multiple downwind distances as suffices with a plume. The navigation systems are tested in a virtual world consisting of a single fixed wind and concentration sensor, a UAV with wind and concentration sensing capability, a uniform wind at a significant fraction of the UAV airspeed, and a simple Gaussian dispersion model. The resulting concentration data is used to characterize the source strength and location by using a genetic algorithm to tune the variables until the model output matches the observations. Tests conducted using randomized source locations indicate that these UAV navigation systems are sophisticated enough to successfully acquire the necessary concentration data in the majority of the cases. The success rate is greatly improved by using an ensemble of non-communicating UAVs and taking the median of the resulting source variables. This process eliminates the outliers that result from occasional navigational failures.

WRF-Solar: Description and Clear-Sky Assessment of an Augmented NWP Model for Solar Power Prediction

AbstractWRF-Solar is a specific configuration and augmentation of the Weather Research and Forecasting (WRF) Model designed for solar energy applications. Recent upgrades to the WRF Model contribute to making the model appropriate for solar power forecasting and comprise 1) developments to diagnose internally relevant atmospheric parameters required by the solar industry, 2) improved representation of aerosol–radiation feedback, 3) incorporation of cloud–aerosol interactions, and 4) improved cloud–radiation feedback. The WRF-Solar developments are presented together with a comprehensive characterization of the model performance for forecasting during clear skies. Performance is evaluated with numerical experiments using a range of different external and internal treatment of the atmospheric aerosols, including both a model-derived climatology of aerosol optical depth and temporally evolving aerosol optical properties from reanalysis products. The necessity of incorporating the influence of atmospheric aer...

A Genetic Algorithm Method to Assimilate Sensor Data for a Toxic Contaminant Release

Following a toxic contaminant release, either accidental or intentional, predicting the transport and dispersion of the contaminant becomes a critical problem for Homeland Defense and DoD agencies. To produce accurate predictions requires characterizing both the source of hazardous material and the local meteorological conditions. Decision makers use information on contaminant source location and transport prediction to decide on the best methods to mitigate and prevent effects. The problem has both observational and computational aspects. Field monitors are likely to be used to detect the release and measure concentrations of the toxic material. Algorithms are then required to invert the problem in order to infer the characteristics of the source and the local meteorology. Here, a genetic algorithm is coupled with transport and dispersion models to assimilate sensor data in order to characterize emission sources and the wind vector. The parameters computed include two dimensional source location, amount of the release, and wind direction. This methodology is demonstrated for a basic Gaussian plume dispersion model and verified in the context of an identical twin numerical experiment, in which synthetic dispersion data is created with the same dispersion model. Error bounds are set using Monte Carlo techniques and robustness assessed by adding white noise. Algorithm speed is tuned by optimizing the parameters of the genetic algorithm. Specific GA configurations and cost function formulations are discussed.

Recent Trends in Variable Generation Forecasting and Its Value to the Power System

The rapid deployment of wind and solar energy generation systems has resulted in a need to better understand, predict, and manage variable generation. The uncertainty around wind and solar power forecasts is still viewed by the power industry as being quite high, and many barriers to forecast adoption by power system operators still remain. In response, the U.S. Department of Energy has sponsored, in partnership with the National Oceanic and Atmospheric Administration, public, private, and academic organizations, two projects to advance wind and solar power forecasts. Additionally, several utilities and grid operators have recognized the value of adopting variable generation forecasting and have taken great strides to enhance their usage of forecasting. In parallel, power system markets and operations are evolving to integrate greater amounts of variable generation. This paper will discuss the recent trends in wind and solar power forecasting technologies in the U.S., the role of forecasting in an evolving power system framework, and the benefits to intended forecast users.