A Fast Differential Deficit Control Volume Approach for Modeling Turbine-Turbine Interactions

Abstract

Several computational tools exist for wind turbine design and wind farm planning/layout ranging from time-intensive computational fluid dynamics (CFD) to lower-fidelity velocity deficit wake models. Analytical and reduced order models (ROMs) are computationally inexpensive but lack the necessary physics to make trend accurate, time-dependent predictions; conversely, high fidelity, Large Eddy Simulations (LES) CFD remains prohibitively expensive and complex for routine wind farm design, optimization and real-time control. This paper presents a one-dimensional, axially symmetric reduced order model that uses integral conservation laws coupled with empirical closure relations for wind turbine wake evolution. I. IntroductionThe 2008 assessment titled 20% Wind Energy by 2030; Increasing Wind Energy's Contribution to U.S. Electricity Supply 8 , investigates the feasibility of a 20% wind generation scenario, which would require mobilization and growth in areas of turbine technology, manufacturing and raw materials, transmission and integration, siting, environmental impacts, and markets. The report was a collaboration of the U.S. Department of Energy and various laboratories including the National Renewable Energy Laboratory and Sandia National Laboratory among others, as well as engineering and consulting firms and the American Wind Energy Association (AWEA) made up of wind manufacturers and suppliers, developers, and electric utilities. The collaborative highlights the theoretical need for immense growth in installed capacity, from 11.6 GW in 2006 to 305 GW in 2030, but concludes the scenario economically viable and identifies initiatives to improve wind turbine performance. These initiatives are aimed at avoiding problems before installation by improving component reliability, full-scale testing methods prior to commercial deployment, validation of design tools and development of standardized design criteria and specifications. More robust methods of performance assurance that can measure, verify, and evaluate individual turbine and largescale wind-plant performance will become increasingly important.