The Impacts of Climate Change via Robust Optimization: Two Applications in Land Investment and Electricity Storage Systems

Abstract

Effectively adapting to a changing climate involves making appropriate operational decisions based on long-term climate forecasts. This dissertation presents a comprehensive framework that combines climate data, regression models, and robust optimization models to examine the decision-making process for adapting to climate change over long time horizons. The research includes two projects: one focuses on studying land investment decisions, and the other investigates the operations of electricity storage systems, both considering the impacts of climate change.

Project 1:

Climate change affects agricultural inputs, like temperature and precipitation, and further affects the economic output of farmland. In this study, we focus on formulating effective policies to aid various stakeholders, including investors and farmers, in adapting to the climate-induced impacts on farmland investment in the Mississippi River Basin (MRB) by using well-known climate models. Each climate model generates a unique climate forecast, and based on these forecasts, we compute a range of farmland values for the MRB. Utilizing these ranges, we apply a robust optimization model to study the optimal investment policies under varying levels of conservatism, representing the extent to which farmland assets are constrained to adopt worst-case values. We show that the optimization model can be linearized and can scale to long time frames, about 50-plus years, and sets of assets. The case study of investment in the MRB covers the years 2023-2090 and uses trajectories of land values determined for each climate scenario using a regression model. Our empirical study shows that there is a disagreement between popular climate forecasts that influence land investment and may affect the most profitable land investments.

Project 2:

The effects of climate change on energy markets are diverse, encompassing changes in demand patterns and supply dynamics, particularly concerning the increasing penetration of renewable energy. These changes impact the dynamics of energy supply from renewable sources, such as wind and solar, leading to increased intermittency. Battery energy storage systems (BESSs) present a promising solution to effectively manage this intermittency from renewable energy sources. However, their profitability and incentive to participate in markets under climate change are susceptible to both the magnitude and frequency of price variation. This project investigates the impact of climate change on a BESS operating in a North American deregulated electricity market. We propose a robust optimization model to determine the operating policy of a BESS over 80 years (from 2021 to 2100) under different climate projections. We reformulate the robust optimization model to an equivalent linear program that allows us to numerically explore the operations of the BESS over the time horizon. Our empirical study analyzes the optimal arbitrage operations of the BESS in the Midcontinent Independent System Operator market in the United States, using the proposed robust model and trajectories of electricity prices determined for each climate scenario by a regression model. Additionally, we introduce a downscaling method to adjust climate scenarios to the desired resolutions for predicting electricity prices through the regression model. The results of the robust model reveal significant variations in the operating incomes of the BESS across different geographical locations and climate scenarios, highlighting the need for tailored strategies adapting to climate-induced variations in energy markets.

The findings from both projects underscore the critical significance of considering a wide range of climate scenarios, encompassing detailed temporal and spatial data when assessing climate adaptation decisions.