This report describes the work done under U.S. Department of Energy grant number DE-FG02-07ER54935 for the period ending July 31, 2010. The goal of this project was to provide predictive transport analysis to the PTRANSP code. Our contribution to this effort consisted of three parts: (a) a predictive solver suitable for use with highly non-linear transport models and installation of the turbulent confinement models GLF23 and TGLF, (b) an interface of this solver with the PTRANSP code, and (c) initial development of an EPED1 edge pedestal model interface with PTRANSP. PTRANSP has been installed locally on this cluster by importing a complete PTRANSP build environment that always contains the proper version of the libraries and other object files that PTRANSP requires. The GCNMP package and its interface code have been added to the SVN repository at PPPL.

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This case study enhances the understanding of open automated demand response opportunities in municipal wastewater treatment facilities. The report summarizes the findings of a 100 day submetering project at the San Luis Rey Wastewater Treatment Plant, a municipal wastewater treatment facility in Oceanside, California. The report reveals that key energy-intensive equipment such as pumps and centrifuges can be targeted for large load reductions. Demand response tests on the effluent pumps resulted a 300 kW load reduction and tests on centrifuges resulted in a 40 kW load reduction. Although tests on the facility?s blowers resulted in peak period load reductions of 78 kW sharp, short-lived increases in the turbidity of the wastewater effluent were experienced within 24 hours of the test. The results of these tests, which were conducted on blowers without variable speed drive capability, would not be acceptable and warrant further study. This study finds that wastewater treatment facilities have significant open automated demand response potential. However, limiting factors to implementing demand response are the reaction of effluent turbidity to reduced aeration load, along with the cogeneration capabilities of municipal facilities, including existing power purchase agreements and utility receptiveness to purchasing electricity from cogeneration facilities.

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