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The U.S. Department of Energy's Clean Energy Manufacturing Analysis Center (CEMAC) provides objective analysis and up-to-date data on global supply chains and manufacturing of clean energy technologies. Policymakers and industry leaders seek CEMAC insights to inform choices to promote economic growth and the transition to a clean energy economy.
This presentation gives an overview of U.S. wind energy development's impacts on wildlife - particularly birds and bats. It includes discussion of mitigation efforts, research collaboratives, and U.S. Department of Energy funding.
This report documents a set of analytical models employed by the optimization algorithms within the GeneratorSE framework. The initial values and boundary conditions employed for the generation of the various designs and initial estimates for basic design dimensions, masses, and efficiency for the four different models of generators are presented and compared with empirical data collected from previous studies and some existing commercial turbines. These models include designs applicable for variable-speed, high-torque application featuring direct-drive synchronous generators and low-torque application featuring induction generators. In all of the four models presented, the main focus of optimization is electromagnetic design with the exception of permanent-magnet and wire-wound synchronous generators, wherein the structural design is also optimized. Thermal design is accommodated in GeneratorSE as a secondary attribute by limiting the winding current densities to acceptable limits. A preliminary validation of electromagnetic design was carried out by comparing the optimized magnetic loading against those predicted by numerical simulation in FEMM4.2, a finite-element software for analyzing electromagnetic and thermal physics problems for electrical machines. For direct-drive synchronous generators, the analytical models for the structural design are validated by static structural analysis in ANSYS.
REopt is a techno-economic decision support model used to optimize energy systems for buildings, campuses, communities, and microgrids. The primary application of the model is for optimizing the integration and operation of behind-the-meter energy assets. This report provides an overview of the model, including its capabilities and typical applications; inputs and outputs; economic calculations; technology descriptions; and model parameters, variables, and equations. The model is highly flexible, and is continually evolving to meet the needs of each analysis. Therefore, this report is not an exhaustive description of all capabilities, but rather a summary of the core components of the model.
A general method for predicting and evaluating the performance of three candidate cross-flow turbine power-maximizing controllers is presented in this paper using low-order dynamic simulation, scaled laboratory experiments, and full-scale field testing. For each testing mode and candidate controller, performance metrics quantifying energy capture (ability of a controller to maximize power), variation in torque and rotation rate (related to drive train fatigue), and variation in thrust loads (related to structural fatigue) are quantified for two purposes. First, for metrics that could be evaluated across all testing modes, we considered the accuracy with which simulation or laboratory experiments could predict performance at full scale. Second, we explored the utility of these metrics to contrast candidate controller performance. For these turbines and set of candidate controllers, energy capture was found to only differentiate controller performance in simulation, while the other explored metrics were able to predict performance of the full-scale turbine in the field with various degrees of success. Finally, effects of scale between laboratory and full-scale testing are considered, along with recommendations for future improvements to dynamic simulations and controller evaluation.
Improvements to wind technologies have, in part, led to substantial deployment of U.S. wind power in recent years. The degree to which technology innovation will continue is highly uncertain adding to uncertainties in future wind deployment. We apply electric sector modeling to estimate the potential wind deployment opportunities across a range of technology advancement projections. The suite of projections considered span a wide range of possible cost and technology innovation trajectories, including those from a recent expert elicitation of wind energy experts, a projection based on the broader literature, and one reflecting estimates based on a U.S. DOE research initiative. In addition, we explore how these deployment pathways may impact the electricity system, electricity consumers, the environment, and the wind-related workforce. Overall, our analysis finds that wind technology innovation can have consequential implications for future wind power development throughout the United States, impact the broader electricity system, lower electric system and consumer costs, provide potential environmental benefits, and grow the U.S. wind workforce.
This report analyzes the storage duration required to reduce VG curtailment under high-VG scenarios. It also examines the value of storage with varying durations.