- Co-Optimization of Reservoir and Power Systems (COREGS) for seasonal planning and operation , ENERGY REPORTS (2022)
- Energy-Storage Modeling: State-of-the-Art and Future Research Directions , IEEE TRANSACTIONS ON POWER SYSTEMS (2022)
- Organic solar powered greenhouse performance optimization and global economic opportunity , ENERGY & ENVIRONMENTAL SCIENCE (2022)
- Using robust optimization to inform US deep decarbonization planning , ENERGY STRATEGY REVIEWS (2022)
- Extending energy system modelling to include extreme weather risks and application to hurricane events in Puerto Rico , NATURE ENERGY (2021)
- Life cycle assessment of salinity gradient energy recovery using reverse electrodialysis , JOURNAL OF INDUSTRIAL ECOLOGY (2021)
- North American energy system responses to natural gas price shocks , ENERGY POLICY (2021)
- Promoting reproducibility and increased collaboration in electric sector capacity expansion models with community benchmarking and intercomparison efforts , APPLIED ENERGY (2021)
- Public acceptance of renewable electricity generation and transmission network developments: Insights from Ireland , ENERGY POLICY (2021)
- Quantification of climate-induced interannual variability in residential U.S. electricity demand , Energy (2021)
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 countyÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢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).