Chris Ordowich, John Chase
Coal and natural gas have and will likely continue to be key components of the world energy supply for years to come. The objective of this study is to evaluate emerging technologies for power generation from coal and gas and forecast their performance using a Power Technology Futures Model (PTFM) based on learning curves methodology. The methodology involves creating a model to assess the levelized cost of electricity (LCOE) from various power generation technologies including supercritical pulverized coal combustion and natural gas combustion as well as advanced alternatives such as integrated gasification combined cycle and carbon capture technologies. Results from the model indicate significant challenges in achieving a 50% reduction in annual CO2 production by 2050, even with substantial R&D investments and carbon pricing strategies. This model serves as a useful tool for planners and policy makers to visualize and communicate the impact of technology investments on future energy generation pathways.
@article{2432bcb3-6698-4bf8-9eed-a500a2144109,
title={Applying Learning Curves to Modeling Future Coal and Gas Power Generation Technologies},
author={Chris Ordowich and John Chase},
year={2023},
language={en}
}TY - JOUR TI - Applying Learning Curves to Modeling Future Coal and Gas Power Generation Technologies AU - Chris Ordowich AU - John Chase PY - 2023 LA - en ER -
This paper addresses the challenge of assessing the feasibility of wind power plant projects at sites with insufficient or no local historic wind data
Important advances in electrochemical engineering technology over the last three decades have fostered the development of a lternative methods to alle
Increasing volumes of waste printed circuit boards from obsolete electronic equipment posed escalating environmental risks and resource losses due to
The leachability tests for manufacturing scrap TV boards (STVB) have indicated the release of metals beyond the limit levels with potential problems f