Abstract 

As the world shifts towards decarbonized energy, accurate modeling tools for capacity expansion modeling (CEM) are vital to facilitate long-term investments in the energy market. CEMs predict future energy markets and estimate the optimal mix of investments to satisfy future energy demands at the lowest possible cost while maximizing social welfare. However, emerging technologies like Long-Duration Energy Storage (LDES) are promising solutions for balancing highly renewable grids (like California), but their accurate modeling in CEMs is a challenge. The framework for temporal resolutions in CEMs does not capture shifting energy across multiple days or weeks, creating a market opportunity for LDES. This dissertation aims to enhance the understanding of input assumptions in CEMs for LDES technologies by evaluating the impact of different modeling horizons and cost assumptions for future LDES technologies. Accurate modeling of LDES in CEMs will lead to better-informed decisions in energy investments. This work highlights the significance of improving LDES modeling in CEMs to accelerate the decarbonization of the energy sector, and it emphasizes the importance of considering emerging technologies while developing CEMs for a sustainable future.

Biography
Pedro received his Bachelor's degree in Renewable Energy Institute from National Autonomous University in Mexico (UNAM). He has participated in multiple solar photovoltaic, wind energy projects and capacity expansion modeling. Current research interests are grid penetration of renewable energies, data-driven solutions to accelerate decarbonization of the power sector, data science, and big data. He believes that with high voltage comes great responsibility.