Engineering topics
Grant, Elenya
Publications and source records attributed to Grant, Elenya.
Wind Turbine Design Optimization for Hydrogen Production
To help meet the need for inexpensive green fuels, we are working on wind turbine design optimization specifically for hydrogen production. We have thus far achieved a 1.53% decrease in LCOH as compared to a turbine optimized for LCOE using the same code, design variables, and models. We accomplished this by optimizing some components of the wind turbine tower, rotor, and drivetrain design with hydrogen production and costs in the design loop.
Achieving gigawatt-scale green hydrogen production and seasonal storage at industrial locations across the U.S
Onsite production of gigawatt-scale wind- and solar-sourced hydrogen (H 2 ) at industrial locations depends on the ability to store and deliver otherwise-curtailed H 2 during times of power shortages. Thousands of tonnes of H 2 will require storage in regions where subsurface storage is scarce, which may only be possible using liquid organic H 2 carriers. We evaluate aboveground system with a focus on providing technical insights into toluene/methylcyclohexane (TOL/MCH) storage systems in locations suitable for gigawatt-scale wind- and solar-powered electrolyzer systems in the United States. Here we show that the levelized cost of storage, at a national median of US dollar $1.84/kg-H 2 is spatially heterogeneous, causing minor impact on the cost of H 2 supply in the Midwest, and significant impact in Central California and the Southeast. While TOL/MCH may be the cheapest aboveground bulk storage solution evaluated, upfront capital costs, modest energy efficiency, reliance on critical materials and pre-sulfided catalysts, and greenhouse gas emissions from heating are opportunities for further development.
Hybrid power plants: An effective way of decreasing loss-of-load expectation
Diversifying variable renewable resources by combining wind, solar photovoltaic, and battery assets in a hybrid power plant can increase renewable energy usage efficiency and improve system flexibility, particularly in distributed energy systems. However, the resilience impact of these systems, particularly outage mitigation, can be difficult to quantify due to uncertainty in resource, energy demand, and outage occurrence. Here, this study outlines a framework to quantify the incremental benefit of hybrid power plant assets for reducing loss-of-load expectation during random outage events. Hybrid power plant performance during outages (considering varying duration and severity) is simulated using a Monte Carlo methodology to reflect uncertainty associated with renewable resource, load demand, and outage timing. Results demonstrate the additional incremental value from increasingly hybrid designs, in which relative capacities of wind, solar photovoltaic, and storage assets contribute to lower loss-of-load expectation than the constituent technologies would alone. The value of added wind or solar capacity increases as the plant composition approaches an equal split. The value of added battery capacity depends on the outage duration and severity, but the first 50 MWh of added storage capacity is the most valuable for reducing the loss-of-load expectation for all plant designs.
Optimal Wind Turbine Design for H2 Production
This presentation covers the annual progress of the H2@Scale project "Optimal Wind Turbine Design for H2 Production". This project investigates the outcomes of designing a wind turbine for hydrogen focused objectives as opposed to the traditional minimization of cost or maximization of energy production. Preliminary results show that significant reductions in the levelized cost of hydrogen can be achieved using hydrogen-specific turbines.
H2Integrate [SWR-23-31]
H2Integrate(H2I) is an open-source Python package for hybrid systems engineering design and technoeconomic analysis. It models and optimizes hybrid energy plants that produce electricity, hydrogen, ammonia, steel, and other products. H2Integrate is designed to be flexible and extensible, allowing users to create their own components and models for various energy systems. The tool currently includes distributed energy generation (wind, solar, wave, tidal), battery storage, hydrogen, ammonia, methanol, and steel technologies. Other elements such as desalination systems, pipelines, compressors, and storage systems can also be included as developed by users. Some modeling capabilities in H2Integrate are provided by integrating existing tools, such as HOPP, PySAM, ORBIT, and ProFAST. The H2Integrate tool is built on top of NASA's OpenMDAO framework, which provides a powerful and flexible environment for modeling and optimization.
Modeling Wind-Hydrogen System and Analyzing Curtailment
Low-cost green hydrogen can be achieved by integrating utility-scale wind farms to an electrolyzer. The power produced by a wind farm depends on spatial and temporal changes in the wind, and this variability can affect the electrolyzer's performance. This study examines the trade-offs between two electrolyzer configurations: a large centralized electrolyzer connected to the wind farm versus several smaller distributed electrolyzers connected directly to each wind turbine. The results show that a centralized configuration generates more hydrogen because turbines that are waked and produce less power are compensated but other un-waked turbines.
Wind Turbine Design Optimization for Hydrogen Production
To help meet the need for inexpensive green fuels, we are working on wind turbine design optimization specifically for hydrogen production. We have thus far achieved a 1.53% decrease in LCOH as compared to a turbine optimized for LCOE using the same code, design variables, and models . We accomplished this by optimizing some components of the wind turbine tower, rotor, and drivetrain design with hydrogen production and costs in the design loop.
Opportunities for green hydrogen production with land-based wind in the United States
Hydrogen (H 2 ) is an efficient energy carrier and storage mechanism that can supply both stationary and transport energy demand. Rapidly declining renewable energy generation costs; technology innovations in wind, solar, battery storage, and electrolysis; and a global push for more sustainable and secure energy have driven increased interest in green H 2 production. In this study, we develop an H 2 scenario analysis tool to assist in rapid, high-resolution insights into future, green H 2 pathways to achieve policy goals and market competitiveness. Using this tool, we estimate H 2 production and costs for U.S., off-grid scenarios given varying policy and cost scenarios from 2025–2035. Results indicate that achieving economically competitive green H 2 production (below $\$$2/kg) is possible in 2030 with no policy incentives (one site achieves this target), while increasing policy support to include wind and green H 2 production tax credits enables widespread economic viability sooner, with sub-$\$$2/kg LCOH targets achieved by 2025 and 51.7% of sites achieving this target by 2035. Maximizing policy support through prevailing wage and apprenticeship credit multipliers enable widespread economic viability, including sub-$\$$2/kg of green H 2 by 2025 and even negative pricing by 2035. Regions with lowest LCOH values correspond to high wind resource areas and capacity factors. Achieving decarbonization goals with green H 2 depends on technology cost reductions and policy support, with a maximum average LCOH reduction of $\$$23.10 between no and maximum policy support scenarios, and a maximum average LCOH reduction of $\$$25.86 between current, conservative technology costs and 2035 projected technology cost assumptions.
Optimal Wind Turbine Design for H2 Production
The Optimal Wind Turbine Design for Hydrogen Production work seeks to address the H2@Scale program's goal to "advance affordable hydrogen production" by optimizing the wind turbine design specifically for hydrogen (H2) production. The project identifies optimal wind turbine designs made specifically for hydrogen production to advance affordable green hydrogen production. The project also couples wind turbine, wind plant, solar plant, and electrolyzer models to predict hydrogen production from variable, renewable power sources.