Research

Job market Paper (forthcoming):

Abstract: Renewable resources are required for energy transition, but not all locations have abundant renewables. Additional transmission provides a potential solution and allows for transfer of renewable power from renewables-rich to renewables-poor areas. I examine the impacts of increased long-distance transfer of electricity on the investment choices of fossil fuel and renewable generators. I detail and estimate a dynamic model for generator behavior consisting of a short-run optimal-dispatch problem for the operations market with consideration of the effects of line losses and transmission constraints between zones and a long run dynamic game for the capacity market. I run counterfactual experiments to analyze the impacts to consumer welfare, emissions, and reliability as well as incentives for investment in renewable energy.

Abstract: High Voltage Direct Current (HVDC) technology is a cornerstone of efficient Offshore Wind Farm (OWF) power transmission. This review examines HVDC OWF integration through four interlinked dimensions: economic considerations, connection topologies, converter designs, and technical modeling. It begins with an in-depth economic analysis, evaluating cost-effectiveness, reliability, and market dynamics, focusing on investment, operational costs, and lifecycle expenses. Building on this foundation, the review explores various collection and transmission architectures, highlighting their technical trade-offs, and evaluates power converter designs for efficiency, reliability, and offshore adaptability. Finally, advanced modeling and simulation techniques are reviewed to optimize system performance, enhance reliability, and balance computational efficiency. Throughout each of the four sections, economic and technical constraints are considered together. This helps to improve understanding of how systems can be designed in a way that meets the constraints of both fields and to improve feasibility on both dimensions. Together, these insights provide a holistic framework for sustainable and economically viable offshore wind energy integration.

In 2020, close to 50% of dividend income in the US was subject to a different marginal tax rate than the top brackets marginal rate. The tax wedge associated with this differential dividend taxation has varied significantly over time since 1983. An unintended side-effect of differential dividend taxation is that it leads to disagreement among shareholders over the level of investment that the firm should undertake. We construct shareholder disagreement measures based on ISS data and dividend taxation and find more disagreement on investment decisions and financial policy decisions relative to other issues (e.g. director elections). Motivated by those observations, we construct a heterogeneous agent, general equilibrium model and show that differential dividend taxation generates disagreement amongst share-holders regarding firm decisions. We demonstrate how to resolve the dis-agreement through voting while maintaing price-taking behavior in the stock market and assuming price conjectures that support competitive, forward-looking investment preferences. Over time the median share-holder changes endogenously, due to exogenous shocks and endogenous capital dynamics. This leads to added fluctuations in capital, increasing the volatility of investment and stock prices.

Data centers have proven a major source of load growth throughout the 21st century. Artificial Intelligence has We analyze the impacts of improvements in artificial intelligence efficiency on power quality and availability in the United States. We further examine the effects of data center power consumption on emissions in the presence of mitigating power procurement policies such as Power Purchase Agreements (PPAs) and Renewable Energy Certificates (RECs).

Commodity markets are inherently more physically-driven and information based than equities markets. The knowledge requirements in commodities has typically made these markets more reliant on text-based information than other markets are. A cottage industry exists of economists using text data to understand commodity market movements. New transformer-based architectures have revolutionized the practice of natural language processing. However, many commodities predictions trained on news sources suffer from a lack of validity due to an inability to separate fact from opinion in news reports. Our paper solves this problem by utilizing the government reports commonly published in commodity markets to train a transformer-based model on a form of text ground truth and then fine-tuning the model on news reports. The model is tested on news reports from out of the sample, and results are reported.

Monocrystalline silicon (c-Si) technology has dominated in the Solar Photovoltaics (PV) manufacturing and the production costs have declined significantly over the past decade. This study investigates the role of learning-by-doing (LBD) in driving this cost reduction and forming the technological pathway of the industry, and its interaction with input price and government subsidy policies.