Essays in optimal resource allocation under uncertainty with capacity constraints

This thesis brings together four papers on optimal resource allocation under uncertainty with capacity constraints. The first is an extension of the Arrow-Debreu contingent claim model to a good subject to supply uncertainty for which delivery capacity has to be chosen before the uncertainty is resolved. The second compares an ex-ante contingent claims market to a dynamic market in which capacity is chosen ex-ante and output and consumption decisions are made ex-post. The third extends the analysis to a storable good subject to random supply. Finally, the fourth examines optimal allocation of water under an appropriative rights system.

Speculation-Aware Resource Allocation for Cluster Schedulers

Real-time demand response is essential for handling the uncertainties of renewable generation. Traditionally, demand response has been focused on large industrial and commercial loads, however it is expected that a large number of small residential loads such as air conditioners, dish washers, and electric vehicles will also participate in the coming years. The electricity consumption of these smaller loads, which we call deferrable loads, can be shifted over time, and thus be used (in aggregate) to compensate for the random fluctuations in renewable generation.

In this thesis, we propose a real-time distributed deferrable load control algorithm to reduce the variance of aggregate load (load minus renewable generation) by shifting the power consumption of deferrable loads to periods with high renewable generation. The algorithm is model predictive in nature, i.e., at every time step, the algorithm minimizes the expected variance to go with updated predictions. We prove that suboptimality of this model predictive algorithm vanishes as time horizon expands in the average case analysis. Further, we prove strong concentration results on the distribution of the load variance obtained by model predictive deferrable load control. These concentration results highlight that the typical performance of model predictive deferrable load control is tightly concentrated around the average-case performance. Finally, we evaluate the algorithm via trace-based simulations.

Essays in mechanism design

This dissertation contains three essays on mechanism design. The common goal of these essays is to assist in the solution of different resource allocation problems where asymmetric information creates obstacles to the efficient allocation of resources. In each essay, we present a mechanism that satisfactorily solves the resource allocation problem and study some of its properties. In our first essay, ”Combinatorial Assignment under Dichotomous Preferences”, we present a class of problems akin to time scheduling without a pre-existing time grid, and propose a mechanism that is efficient, strategy-proof and envy-free. Our second essay, ”Monitoring Costs and the Management of Common-Pool Resources”, studies what can happen to an existing mechanism — the individual tradable quotas (ITQ) mechanism, also known as the cap-and-trade mechanism — when quota enforcement is imperfect and costly. Our third essay, ”Vessel Buyback”, coauthored with John O. Ledyard, presents an auction design that can be used to buy back excess capital in overcapitalized industries.