Dr. Jeremiah Johnson joined NC State’s Department of Civil, Construction & Environmental Engineering in 2017 as an associate professor and as part of the faculty cluster focusing on Sustainable Energy Systems and Policy. His research uses systems methods to evaluate the environmental impacts of changes to the power and transportation systems, including those driven by technology (such as the integration of wind power, solar photovoltaics, and energy storage) and policy. Dr. Johnson has projects funded by the National Science Foundation, the Department of Energy, and the Sloan Foundation. At NC State, he teaches courses related to sustainable infrastructure and renewable energy. He earned a Ph.D. and an M.S. from Yale University in environmental engineering and a B.S. in chemical engineering from Clarkson University. He is originally from the foothills of the Adirondack Mountains in New York.
SHORT DESCRIPTION OF INTERESTS:
I am an environmental engineer who conducts research on the environmental impacts of emerging technologies, with a focus on the electric power and transportation sectors. My projects use life cycle assessment, optimization of power system operations and planning, and building-level controls experiments. I am interested in contributing to projects that use systems approaches for climate mitigation and adaptation.
- Life Cycle Greenhouse Gas Emissions of CO2-Enabled Sedimentary Basin Geothermal , ENVIRONMENTAL SCIENCE & TECHNOLOGY (2024)
- The Sub-Metered HVAC Implemented for Demand Response Dataset , JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME (2024)
- Accelerating China's power sector decarbonization can save lives: integrating public health goals into power sector planning decisions , ENVIRONMENTAL RESEARCH LETTERS (2023)
- Planning for winter peaking power systems in the United States , Energy Policy (2023)
- Tapping the Unused Energy Potential of Solar Water Pumps in India , ENVIRONMENTAL SCIENCE & TECHNOLOGY (2023)
- The Health and Climate Benefits of Economic Dispatch in China's Power System , ENVIRONMENTAL SCIENCE & TECHNOLOGY (2023)
- The Health and Climate Benefits of Economic Dispatch in China?s Power System , ENVIRONMENTAL SCIENCE & TECHNOLOGY (2023)
- The promise of coupling geologic CO2 storage with sedimentary basin geothermal power generation , ISCIENCE (2023)
- Diverse Pathways for Power Sector Decarbonization in Texas Yield Health Cobenefits but Fail to Alleviate Air Pollution Exposure Inequities , ENVIRONMENTAL SCIENCE & TECHNOLOGY (2022)
- Energy-Storage Modeling: State-of-the-Art and Future Research Directions , IEEE TRANSACTIONS ON POWER SYSTEMS (2022)
The University of Michigan and partners at North Carolina State University and Ohio State University propose the research project ÃƒÂ¢Ã¢â€šÂ¬Ã…â€œCO2 Utilization for Geothermal Energy Production and Renewable Energy StorageÃƒÂ¢Ã¢â€šÂ¬Ã‚Â to address the need to develop and deploy carbon negative energy technologies. The research team led by Brian Ellis (PI) includes Co-PIs Jeffrey Bielicki (Ohio State University) and Jeremiah Johnson (North Carolina State University), and industrial collaborator, TerraCOH. The PIs will combine their complementary expertise in the geochemistry of geologic carbon dioxide (CO2 ) storage (GCS), geothermal reservoir engineering, systems-scale techno-economic modeling and optimization, and power systems modeling to provide technical advancements that will enable the deployment of novel CO2-based geo-energy systems.
The United States must find policy solutions that enable deep decarbonization of the energy system in order to mitigate the worst effects of climate change. Appropriate action will require fundamental changes in the way we produce and consume energy. Policy makers face the monumental challenge of crafting effective climate policy in the face of deep future uncertainty. Computer models of the energy system ÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“ referred to as energy system models ÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“ provide a way to examine future energy system evolution and test the effects of proposed policy. Unfortunately, many of these computer models are opaque to outsiders and are used to run a few scenarios that produce limited insight. Given the stakes associated with climate change mitigation, we must do better. Our project aims to bring energy modeling into the twenty-first century by applying the gold standards of policy-focused academic modeling, maximizing transparency, building a networked community, and working towards a common goal: examining U.S. energy futures to inform future energy and climate policy efforts.
This project will develop new methods to mitigate adverse human health impacts from power sector emissions through the targeted use of grid-connected energy storage. Energy storage devices, such as batteries or pumped storage hydropower, can shift both the time and location of power sector emissions based on their charging and discharging strategies. The overall human health impacts of criteria pollutants such as SO2 and NOx are closely related to the both temporal and spatial distribution of emissions. The overarching research question that will be answered is: Can operational strategies for grid-connected energy storage yield cost-effective reductions in the human health impacts associated with power sector emissions? To answer this question, the research team will develop a unit commitment and economic dispatch model with energy storage to determine optimal power system operations and provide unit-level SO2 and NOx emissions. A reduced-form air pollution transport model will provide spatially- and temporally-resolved PM2.5 and O3 concentrations stemming from power plant and energy storage dispatch decisions. Human health damage cost estimates will determine the health response from changes in exposure to these secondary pollutants, coupling those results with the value of a statistical life and determine the unit-level marginal health damage costs associated with the primary emissions. Those costs then serve as inputs into the power system model to allow real-time decision making, effectively internalizing the externality costs of the emissions and yielding the optimal charge/discharge behavior of the energy storage to cost-effectively reduce human health impacts.
The global demand for plastics is 400 million metric tons and is projected to triple by 2050. However, only 8.4% of plastics are recovered in the U.S., and the current supply of reprocessed plastics only meets 6% of the demand for plastic products. Improved plastics recycling is an important part of building a more circular economy due to the increasing demand for plastic products. However, cost-effective recovery of plastics is limited by separation efficiency, contamination, available markets, and quality degradation during collection, separation, and conventional mechanical reprocessing. Given these issues, there is a need for cost-effective technologies to efficiently recover plastics in municipal solid waste. Chemical recycling is an emerging and potentially scalable alternative to convert all types of waste plastics into virgin-quality resins. However, little is known about the economic and environmental implications of chemical recycling because it is newer and less developed than conventional mechanical recycling. The goal of the proposed project is to comprehensively assess new strategies and technologies for plastic recycling. The proposed project aims to provide practical recommendations for the solid waste industry to cost-effectively improve the plastics recycling rate and associated material quality while reducing environmental burdens. The research objectives are: 1. To develop and implement life-cycle process models to quantify the costs and life-cycle environmental impacts of various plastics recycling technologies 2. To assess and compare the environmental and economic trade-offs associated with alternative plastic waste management strategies 3. To evaluate the potential for scale-up of emerging technologies at multiple stages of plastic waste management strategy planning The objectives will be achieved using life cycle assessment (LCA) and life cycle costing (LCC) methodologies to quantify and compare the cost and environmental impacts of plastic recycling alternatives. Strategies to be considered will include conventional mechanical recycling, polymer recycling (glycolysis and methanolysis), monomer recycling (pyrolysis/gasification), and plastics-to-fuel for energy recovery.
We will develop a full roll-out model, based on validated planning and simulation tools, that is able to model the deployment of a wide range of propulsion and energy storage technologies in the Class 1 Rail Freight sector and that determines associated lifecycle GHG emissions and levelized cost of Mt-km (LCOTKM) values over various time scales (e.g., 10, 20, 30 years). Our work will include: (1) microscale train simulation; (2) network train simulation; (3) identification and characterization of infrastructure requirements; (4) identification and characterization of decarbonized energy pathways; (5) probabilistic cost modeling; (6) freight demand scenarios; (7) technology transfer and outreach; and (8) integrated assessment. The latter will include case studies based on application of the developed, detailed case studies of specific lines, settings, and situations, with extrapolations to the whole network, inclusive of coupling with infrastructure, decarbonized energy pathways, demand scenarios, and cost. We will appoint an advisory board to facilitate technology transfer and outreach.
Revamping the electric grid to get to net-zero greenhouse gas emissions by 2050 has become an important international goal to avoid life-threatening environmental damage. The wide scale deployment of variable renewable energy technologies (VREs) offers a pathway to decarbonize the electric grid. The contribution of VRE to resource adequacy as a function of VRE penetration across several technologies is discussed using the effective load carrying capability (ELCC) method to calculate CC values for regions of the contiguous United States. As the deployment of VRE increases, we show that its marginal contribution to meeting peak load does not stay constant as assumed in the past by many resource planners in the energy industry. The correlations of plant outages due to events such as extreme temperature, drought, and fuel supply restrictions are also explored, thus yielding more realistic resource adequacy results that can cultivate more economical long-term resource planning for deep-decarbonization.
We will take a two-pronged approach to assess the impact of demand response (DR) on energy efficiency. 1) Using whole building electric load data corresponding to baseline and demand response operation along with energy efficiency audits from over 500,000 buildings in Northern California we will assess the impacts of DR on long-term energy efficiency trends. This builds on our preliminary work using data from a small collection of buildings which found that as buildings become more energy efficient they have less potential for demand response, but implementing demand response strategies in buildings generally improves their efficiency in the long run. 2) We will conduct experiments on multiple buildings on the University of Michigan and SLAC/Stanford campuses to assess the short-term energy efficiency implications of load shifting for grid ancillary services on building energy consumption. Our preliminary experimental work has shown that even when made to purely shift (not shed) load buildings increase energy use compared to their baselines. Existing building models do not capture this phenomenon.
China has started a new round of power market reform, introducing a dispatch approach that minimize the electricity generation costs to its current power sector. Although several studies have investigated the carbon emission impacts of adopting this economic dispatch in China, none have estimated the human health impacts brought by this transition. Comprehensively understanding the impacts of the power market reform will provide insights on how to make better regulations to protect the public health. This project will estimate the health impacts by integrating power system models and air quality models, and also explore how to cost-effectively reduce these health impacts by internalizing real-time health costs in plant dispatch decisions and re-optimizing the unit commitment and economic dispatch in light of these impacts.
To empower the dynamic farming community of rural Chhattisgarh (India), the government has launched a wide-scale deployment of solar water irrigation pumps under a government program titled Saur Sujala Yojna. Thousands of pumps have already been distributed and over the next two years, 51000 farmers across the state are expected to benefit from the program. A minor survey and anecdotal evidence from farmers in the state indicates that these pumps are being under-utilized and their operation is not co-optimized with water table levels and crop rotation needs. Our proposed project brings together a unique and comprehensive team of researchers at NC State University and NIT-Raipur to address this very important research problem. Our proposed research investigates methods to ensure optimal utilization of these pumps by developing a comprehensive research framework. We plan to carry out a large-scale survey of farmers across the state to identify key challenges faced by them with respect to these pumps and develop a prototype system at NIT-Raipur which will be used to simulate various scenarios and gather relevant data. Using the data, we will develop robust methods that mitigate inefficient pump use. The results will be condensed to provide technical and policy recommendations to key government organizations in Chhattisgarh. They will also be disseminated in the form of peer-reviewed publications for the global research community.
The central goal of this project is to answer the question: What are the environmental impacts of providing power system reserves with distributed energy storage and how can operational strategies influence environmental outcomes? In the past year, we completed a study in which we coupled cradle-to-gate and end-of-life life cycle assessment data on Li-ion batteries with a unit commitment and dispatch model of a 9-bus power system with energy storage used for frequency regulation. This study also incorporated detailed treatment of battery degradation testing two different degradation models.