A cost-effective pathway towards net-zero electric power systems requires an extraordinary deployment of new solar and wind generation assets. This aggressive expansion driving unprecedented investment entails a fundamental understanding of the challenge ahead of us. My doctoral dissertation seeks to provide a multidisciplinary perspective of research questions that shine the light on rapid and cost-efficient strategies for the energy transition. Integrating operations research and geospatial analysis methods, the dissertation utilizes a multidisciplinary approach when addressing three questions.

First, the dissertation examines the role of battery energy storage technologies (i.e., utility-scale lithium-ion batteries) on reducing the greenhouse gas emissions of an electric power system while simultaneously achieving a reduction in carbon abatement costs. The study uses a cost-based production model (day-ahead unit commitment and a real-time economic dispatch) to simulate the optimal operation of all the generation resources in the largest vertically-integrated electric service region in the U.S. The study explores a multitude of configurations to identify optimal sizing of battery energy storage systems when paired with utility-scale photovoltaics.

Next, the dissertation studies the effect of incorporating high-resolution data when identifying suitable land for renewable energy projects over a geographically defined region. Using a python-based user-friendly siting tool implemented in ArcGIS Pro to perform suitability and cost analysis of utility-scale photovoltaic projects in North Carolina under three scenarios (representing conditions ranging from favorable to restrictive). The study finds that the land suitable for utility-scale photovoltaics reduces substantially when parcel-level data reflecting local land use restrictions is incorporated. The study’s findings highlight the necessity of integrating detailed land-use data that reflects local regulation (zoning ordinances) into siting models while simultaneously increasing their spatial granularity.

Lastly, the dissertation analyzes the benefits of weatherizing wind power farms enabling their operation under extreme climates (winter storms). The study uses global reanalysis data with operational information from the Electric Reliability Council of Texas (ERCOT) during the 2021 winter storm Uri to simulate a continued operation of wind power farms under low-temperature environments.  The study finds that the financial benefits that winterized wind turbines would have received during winter storm Uri would have outweighed the capital costs required to implement ice-accretion mitigation actions (before winter storm URI).