Connor Colombe

Many model-based decarbonization pathways include substantial carbon capture, utilization, and storage (CCUS), and engineering cost estimates suggest that these technologies should be profitable to deploy under current incentives, such as the 45Q tax credits in the United States. However, CCUS investments have been slow to materialize. In this paper, we investigate the extent to which policy uncertainty and investor risk aversion may explain the limited buildout of CCUS infrastructure to date. To do so, we develop a two-stage stochastic programming model for optimal CCUS infrastructure network design that incorporates policy uncertainty and risk aversion as novel features. We then apply it to an empirically parameterized case study of the Texas-Louisiana Gulf Coast region. Our results show that the current 45Q incentive levels make a fair number of CCUS projects profitable even if they were to be discontinued after ten years. Moreover, while future policy uncertainty and risk aversion reduce CCUS development, the maximum effect size is only a 16% decline in expected total CO2 captured. We find policy uncertainty plays a more important role than investor risk aversion in the development of CCUS infrastructure, with increased uncertainty leading to reduced CO2 capture and investment.

In this paper, we investigate the resilience of alternative electric grid configurations by adopting a stylized approach based on graph theory, probabilistic analysis, and simulation. We consider two alternative classes of electricity network topology: binary trees and rectangular lattices. For each topology, we find the probabilities that customers located at various nodes in the network will continue to have power following a disaster, depending on the locations of resources (e.g., generators, storage units) in the network. Then, these probabilities are incorporated into the problem of optimally placing resources throughout the network. This is a cost–benefit problem that weighs the benefits of placing resources closer to customers – that is, pursuing a distributed resilience strategy – against the higher total cost of deploying a greater number of smaller resource units. Our analytical and numerical results thus shed light on the general circumstances in which centralized or distributed resilience strategies are preferable. While optimal resource placements depend on various assumptions, such as the probability that power lines fail and the strength of economies of scale, we find that distributed resilience strategies are more often preferred in the binary tree topology than in the rectangular lattice topology. Rectangular lattices feature greater redundancy in terms of paths between nodes in the network, enabling the system to be fairly resilient even with centralized resources.

We describe the first strongly subquadratic time algorithm with subexponential approximation ratio for approximately computing the Fr}’echet distance between two polygonal chains. Specifically, let \P and \Q be two polygonal chains with \n vertices in \d\-dimensional Euclidean space, and let {}alpha }in [}sqrt{n}, n]\. Our algorithm deterministically finds an Ø(}alpha)\-approximate Fr}’echet correspondence in time Ø((n^3 / }alpha^2) }log n)\. In particular, we get an Ø(n)\-approximation in near-linear Ø(n }log n) time, a vast improvement over the previously best know result, a linear time \2^{O(n)}\-approximation. As part of our algorithm, we also describe how to turn any approximate decision procedure for the Fr}’echet distance into an approximate optimization algorithm whose approximation ratio is the same up to arbitrarily small constant factors. The transformation into an approximate optimization algorithm increases the running time of the decision procedure by only an Ø(}log n) factor.