Introducing the Renewable Energy Network Optimization Tool (ReNOT): Part I

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
Wednesday, 26 January 2011: 2:15 PM
Introducing the Renewable Energy Network Optimization Tool (ReNOT): Part I
4C-4 (Washington State Convention Center)
Randall Alliss, Northrop Grumann Corporation, Chantilly, VA; and R. Link, D. Apling, M. L. Mason, H. Kiley, G. Higgins, and K. Darmenova
Manuscript (2.4 MB)

As the renewable energy industry continues to grow so does the requirement for atmospheric modeling and analysis tools to maximize both wind and solar power. Renewable energy generation is variable however; presenting challenges for electrical grid operation and requires a variety of measures to adequately firm power. These measures include the production of non-renewable generation during times when renewables are not available. One strategy for minimizing the variability of renewable energy production is site diversity. Assuming that a network of renewable energy systems feed a common electrical grid, site diversity ensures that when one system on the network has a reduction in generation others on the same grid make up the difference. A similar problem faces government organizations interested in space to ground laser communications. Clouds severely attenuate a free space optical communication (FSOC) signal therefore in order to achieve maximum link performance one must use site diversity techniques to find optimal ground stations. We have developed and applied the Lasercom Network Optimization Tool (LNOT) modeling system to perform site selection and availability trade studies. This has been accomplished by the development of a fifteen year climatological and high resolution cloud database based on geostationary satellite imagery. The site-diversity strategy we developed for laser communications can be used to mitigate the intermittency in alternative energy production systems while still maximizing saleable energy. Recently, LNOT has been adapted to optimally site potential wind turbine and solar collector farms. The adapted system is referred to as the Renewable Energy Network Optimization Tool (ReNOT). Although the problem is different than for FSOC, the modeling framework is easily extendable. The new system has a plug-in architecture that allows us to accommodate a wide variety of renewable energy system designs and performance metrics. For example, one might optimize site locations to maximize day ahead predictable power all the while accounting for short term variability. The cloud database required to run ReNOT have been developed originally for the FSOC problem. Fifteen years of GOES imagery over the Continental United States and Hawaii have been run through a custom cloud retrieval algorithm to provide cloud information at 4km horizontal and 15 minute temporal resolution, respectively. Our existing high-resolution cloud cover database is coupled with a sophisticated solar irradiance model to provide the basic databases needed for the site selection of solar energy farms. In addition, we are modeling the orientation geometry of panels from design specifications of installed panels with a pointing algorithm. The Weather Research and Forecasting (WRF) mesoscale model is applied to generate high-resolution wind databases to support the site selection of wind farms. These databases are generated on High Performance Computing systems such as the Rocky Mountain Supercomputing Center (RMSC). The databases are then accessed by ReNOT and an optimized site selection is developed. We can accommodate numerous constraints (e.g., number of sites, the geographic extent of the optimization, proximity to high-voltage transport lines, etc.). In general, unconstrained optimization over a large geographical area leads to an enormous number of combinations of sites, making an exhaustive search infeasible. The ReNOT modeling system uses a unique stochastic optimization algorithm to reduce the computational burden to manageable levels. The ReNOT system is run on a high performance computing cluster. The optimization can be further used to evaluate the benefit of joint solar and wind generation networks. Finally, the development of high resolution regional climate simulations through dynamic downscaling is being performed to understand future wind, cloud, and temperature patterns and their impacts on existing and future renewable energy production capability. Running ReNOT on these future data sets allows us to select sites optimized for tomorrow's climate, rather than yesterday's. Part II of this paper will present case studies of both a wind and solar farm optimization.