Joseph DeCarolis

The objective of this research is to develop semi-transparent organic solar modules integrated with greenhouses along with engineered plant photo-action spectra that synergistically provide food and energy sources while conserving water for a new food-energy-water paradigm.

Continually increasing water demand (due to population growth) and fuel costs threaten the reliability of water and energy systems and also increase operational costs. In addition, both natural climatic variability and the impacts of global climate change increase the vulnerability of these two systems. For instance, reservoir systems depend on precipitation; whereas power systems demand depend on mean daily temperature. Currently, these systems use seasonal averages for their short-term (0-3 months) management, which ignores uncertainty in the climate, thereby resulting in increased spillage and reduced hydropower. While seasonal climate forecasts contain appreciable levels of skill over parts of the US in both winter and summer, the uptake of these forecasts for water and energy systems management has been limited due to lack of a coherent approach to assimilate probabilistic forecasts into management models. We systematically analyze various scenarios that aim at improving the performance of these systems utilizing the multimodel climate forecasts and a high performance computing (HPC) framework.

The primary goal of this proposed Science Across Virtual Institutes (SAVI) effort is to establish a self-sustaining virtual institute to enhance research, education, and outreach related to life-cycle assessment (LCA) of solid waste management (SWM) systems.

The US EPA is interested in developing a next generation tool for sustainable materials management as an update to the current Municipal Solid Waste Decision Support Tool (MSW DST). As part of this task, RTI International (RTI) will conduct a review the current SWOLF software tool being developed at North Carolina State University (NCSU) and assess the work required to convert the tool into a stand-alone desktop application, similar to the MSW DST.

The overall objective of the proposed project is to assist the Wake County Solid Waste Division with long-term planning for SWM. SWOLF, a solid waste life-cycle model developed at NCSU, will be utilized to model the county������������������s current solid waste system and to explore and evaluate future alternatives in consideration of appropriate and county-specific preferences and constraints.

The FREEDM Center is developing critical smart grid technologies that can enable the large scale deployment of renewables on the electricity distribution network. The purpose of this project is to assemble estimates of costs and benefits for FREEDM components in order to refine the cost-benefit model developed last year.

The goal of this proposal is to develop new EEO methods to enable a system-wide assessment of energy technology and public policy aimed at delivering deep cuts in greenhouse gas and air pollutant emissions. This goal motivates the following research objectives: (1) develop open source datasets at both the U.S. national and multi-region global level to address questions related to the environmental and economic impacts of proposed energy and environmental policy, (2) utilize multi-core and compute cluster environments to enable rigorous uncertainty analysis, and (3) develop a joint cognitive process to allow the efficient interaction of decision makers and computer models to produce new policy-relevant insight. These research objectives will be tightly integrated with an educational plan that uses EEO models as a tool to teach students to think critically about energy technology assessment as well as energy and environmental policy from a systems perspective.

This research will enable accurate assessment of the energy use and emissions of plug-in hybrid electric vehicles (PHEVs) at high spatial and temporal resolution, as well as at regional and national scales, using consistent data and coordinated approaches. The EU&E impacts will account for consumption of fossil fuels (gasoline, diesel), biofuels (ethanol, biodiesel), and electricity. The main objectives are to: (1) develop and demonstrate a micro-scale methodology for measuring and modeling the real-world activity, energy use, and emissions of PHEVs at high temporal and spatial resolution; (2) develop and demonstrate a methodology for quantifying the impact of electrical demand for PHEV recharging on regional and national power generation; (3) similarly, assess the impact of biofuel demand for biofueled PHEVs (B-PHEVs) on tailpipe and indirect emissions; and (4) apply the new multiple-tiered micro and macro-scale framework to a case study to demonstrate its applicability to technology assessment and policy planning.

The goal of the proposed research is to investigate the cost and environmental implications of emerging greenhouse gas (GHG) reduction policies on the solid waste management (SWM) sector, and to outline alternative ways in which municipal solid waste (MSW) managers may optimally respond through changes to material flows and choice of process-technologies in SWM systems. This will be accomplished through the following research objectives: 1. Evaluate the changes over time in technology choices for individual MSW processes (e.g., collection, landfills, and composting facilities) that would most cost-effectively respond to climate change mitigation policies. For example, there are multiple technologies available for aerobic composting and landfill gas collection with varying levels of GHG emissions, and their cost-effectiveness will vary in response to climate change mitigation policies. 2. Evaluate the changes in integrated SWM strategies (i.e., waste flows and process choices) that would most effectively respond to different climate change mitigation policies. For example, as the price of energy and GHG emissions increases, the cost-effectiveness of each MSW process may change by varying degrees, affecting cost-effective combinations of integrated SWM programs designed to meet climate change mitigation goals. 3. Evaluate and determine the effects of climate change policies on other environmental emissions and impacts associated with integrated SWM strategies responding to climate mitigation policies. For example, a SWM program that responds to GHG emissions reduction policies may adversely impact other environmental considerations such as acidification potential, water consumption, and ozone depletion. 4. Evaluate and determine the interdependent effects of specific policies designed to influence SWM in a carbon regulated environment. For example, waste combustion is banned in some jurisdictions yet this encourages an inferior technology (landfills) with respect to GHG emissions.

The goal of the proposed research is to develop a life-cycle assessment (LCA) model capable of analyzing solid waste management (SWM) performance ? at both the individual process and integrated system levels ? taking into account implications of greenhouse gas (GHG) mitigation policies and competing SWM objectives (e.g., costs, emissions, and diversion targets). An integrated life-cycle optimization model will be developed to estimate the costs, energy use, emissions, and environmental impacts associated with the processes (e.g., collection, separation, waste-to-energy [WTE], composting, anaerobic digestion, landfilling) that constitute the SWM system. The model will be used to meet the following objectives: 1. Quantify the increased costs associated with various SWM processes due to different GHG mitigation policies including anticipated energy price changes induced by these policies. 2. Evaluate changes in integrated SWM strategies (i.e., waste flows and process choices) that most effectively respond to different GHG mitigation policies. 3. Quantify the effects of GHG mitigation policies and related energy price changes on other SWM-related environmental impacts (e.g., smog formation and acidification).

The objective of the proposed research is to develop an improved data set and model to predict methane production from U.S. landfills.

Although crop and timber-based biomass have traditionally been viewed as primary choices for biofuel production, recent research suggests that municipal solid waste (MSW) may overcome a number of limitations, such as limited availability and accessibility, as compared to first-generation feedstocks . North Carolinians generate more than 9.9 million tons of MSW per year according to the N.C. Dept. of Environment and Natural Resources. Roughly 60% of this mass consists of paper, plastic, and food scraps that can be converted into biofuels via gasification technologies. If all of the MSW generated in N.C. were directed towards biofuels production, this feedstock would generate 297 million gallons of biofuels (assuming a conservative conversion rate of 50 gal biofuel per ton MSW), which is roughly 5% of the State?s total fuel consumption annually . This quantity would meet half of the state?s 2017 goal to have 10% of liquid fuels come from N.C. produced biofuels. However, the concept of converting MSW into liquid biofuels is relatively new. As a result, the mechanisms and infrastructure needed to make this technology a viable industry are poorly understood. Thus, the primary objectives of this proposal are to: 1. Compare conversion of MSW into biofuels with current methods used to generate electricity/heat from MSW (e.g. landfill gas to energy, waste-to-energy). 2. Summarize MSW management and operational infrastructure and evaluate how it can be used to support needed infrastructure for the biofuels industry. 3. Develop a report that can be used to educate the public and policymakers about the biofuel production feasibility/capability using MSW as a feedstock. To achieve these objectives, the Environmental Research and Education Foundation (EREF) and its partners (NC State University, Maverick Biofuels, Waste Industries) will conduct a meta-analysis to acquire data related to: process conversion efficiencies, processing capacity, environmental metrics, energy production, waste quantity and composition, and geospatial factors (Obj. #1). This data will then be used to examine MSW management infrastructure (Obj. #2) and develop information that can serve as a basis for a plan for integrating the use of MSW as a biofuel feedstock (Obj. #3). Issues such as waste reduction initiatives, shifts in waste composition, and their effect on the suitability of MSW as a biofuel feedstock will be considered. The primary deliverable from this $93,119 project will be a report describing the study findings. The project will be completed in 16 months. The EREF?s stakeholder base represents the majority of the solid waste industry. Thus, the EREF has the ability to leverage these results to support MSW to biofuel conversion technologies in NC and beyond.

North Carolina State University (NCSU), in partnership with North Carolina community colleges, submits this proposal which builds upon and extends accomplishments being realized in a CCLI Phase 1 project?Green Research for Incorporating Data in the Classroom (GRIDc). The Phase 1 project is leading to the development of new curricula to teach STEM concepts associated with renewable energy technologies, and is generating avenues for adding new technologies to the data grid, diffusing more curricula, and expanding research partnerships. The overarching goal of this Phase 2 project is to develop undergraduate students? higher order thinking skills in the context of a data-rich learning environment. Three principle objectives of the project are: 1) creating learning materials and teaching strategies, 2) assessing student achievement and higher-order thinking skills, and 3) implementing educational innovations. GRIDc II will collect real-time data on renewable energy technologies from multiple systems (e.g., solar, wind, electric, alternative fuels), store the data at a central location, and upload the data to an Internet-based data acquisition system where the data may be accessed by faculty and undergraduate students for use in classroom instruction. The Solar House at NCSU, one of the most visible and visited solar buildings in the United States, provides the location for many of the renewable energy technologies with additional technologies in North Carolina being added to the data grid system. Progress Energy and Advance Energy will provide access to data from renewable transportation technologies. Curriculum materials and instructional units developed by faculty participating in this project will have broad application in undergraduate science, technology, engineering and mathematics education, advancing students? factual, conceptual and procedural knowledge, application of knowledge to problem solving and decision making, and metacognitive understanding of how they ?learn to learn?. Assessment instrumentation measuring students? common core of knowledge associated with renewable energy technologies will be validated for use in varied undergraduate courses at multiple 4-year and 2-year institutions. Partnerships with North Carolina community colleges will create new avenues for post-secondary collaboration. An independent evaluation of the project will support continuous improvements in achieving the project goal and objectives. Intellectual Merit. GRIDc II will establish a cyber learning environment where varied STEM learners access real-time data for use in classroom instruction. Faculty in the STEM classes will develop an understanding for the effectiveness of an integrated, data-rich curriculum to teach STEM concepts and support students? cognitive skills. The data grid, associated curricula, and assessment instrumentation will be widely available for researchers to further the study of students? higher order thinking, problem-solving and decision making processes in the STEM disciplines. Contributing to the intellectual merit, the content of this proposal is socially relevant in light of the nation?s need to develop renewable energy technologies. The qualifications of the project team and resources at the partnering institutions ensure a successful project that will advance NSF STEM objectives and the science of learning.

While global interest in sustainable buildings has driven engineering innovations in building-related technology, considerably less attention has been paid to the effectiveness of the prevailing building design process. Computer simulations of building performance to assess the impact of design choices on energy, water, and material consumption as well as emissions are only loosely coupled to the architectural design process. Further, building simulation models only provide point estimates of performance, relying on users to make manual changes to inputs and rerun the model. The result is a design process driven by informed trial-and-error rather than quantitative building-specific performance data, which makes design for sustainability difficult. We hypothesize that the availability of targeted building performance data through the full breadth of the architectural design process will lead to innovative designs that improve the use of energy, water, and materials during both building construction and operation. The goal of this research is to evaluate a human-computer joint cognitive design process, allowing architects to couple building information models (BIM) with building performance analysis to create a progressive decision-making framework for building design. To meet this goal, we will test an approach to find good configurations of building options to meet performance objectives specified at any stage of the design process. This search will explore an array of alternatives that underscore the multiplicity of design possibilities available to match the design objectives.