Browsing by Subject "Auctions"

Now showing 1 - 4 of 4

Capacity auctions for electricity
(2018-12) Yucel, Emre; Dyer, James S.; Butler, John; Muthuraman, Kumar; Anderson, Edward
Faced with uncertainty of future electricity generation supply, many regional electricity markets have adopted or considered adopting capacity markets for electricity. We study the structure of these markets and in particular capacity supply auctions such as the one implemented by PJM Interconnection (PJM), a regional transmission organization. Participants bid generation capacity into the auction, and those that win receive a capacity payment in return for having this capacity available for generation at a future delivery date. The auctions can be classified as multi-unit uniform price auctions, though price is set according to a demand curve rather than by participants' bids. We find closed-form solutions for the optimal bids as a function of cost, study welfare impacts of the auction, and show how the results can be extended numerically for more complex situations. We then use these optimal bid functions in an agent-based simulation of electricity markets, comparing energy-only markets to capacity markets and measuring the impact on both the generators and consumers of electricity. Lastly we use our agent-based simulation model coupled with reinforcement learners to determine whether or not the optimal bid strategy discovered in the beginning can be learned over time by agents participating in the energy and capacity markets.
Essays on multi-item auctions : theorectical and empirical investigations
(2011-08) Fu, Rao; Sibley, David S. (David Summer), 1947-; Hendricks, Kenneth; Gu, Bin; Wiseman, Thomas; Miravete, Eugenio
In this dissertation, I explore bidders’ behavior in multiple auctions which are conducted sequentially or simultaneously. The first and the second chapters examine buyers’ bidding behaviors in an environment of multiple simultaneous auctions and show that the wildly-used assumption of proxy bidding is inappropriate in the multiple auction setting. The first chapter proposes two models which try to describe online auction platforms. One model has a fixed ending time and the other does not. I show that incremental bidding strategy can arise out of equilibrium and weakly dominate the proxy bidding strategy. Late bidding is also discussed. I use the data I collect from eBay to test these theoretical predictions in the second chapter. The estimation results basically support the theory part. Incremental bidders who switch among different auctions are more likely to win and have higher payoffs than proxy bidders. The third essay studies the procurement auctions in the Texas school milk market. I define score functions to map the bids from multiple dimensions to one dimension and analyze the factors that may affect the bids of school milk suppliers. After considering the impacts of these factors including backlogs and cost synergies, I can still find some evidences for existence of collusion among the bidders.
Modular autonomous intersection management simulation for stochastic and priority auction paradigms
(2021-12-03) Liao, Carlin; Boyles, Stephen David, 1982-; Claudel, Christian; Kumar, Krishna; Stone, Peter
Automated intersections, when combined with the proliferation of autonomous vehicles (AVs), allow for more precise and innovative methods to control traffic at these integral choke points in the road system. In this dissertation, I develop a refined, modular framework for autonomous intersection management (AIM) simulation and implement it as a software library with robust documentation and testing to support present and future research in this field. Demonstrating this framework's efficacy, I apply it to study two topic areas in the AIM space: stochastic movement and priority auctions. Stochastic AIM is introduced as an extension of traditional AIM that permits probabilistic reservations of space and time in an intersection. Its use case is motivated by the integration of human-driven vehicles into AIM using augmented reality guidance to behave more accurately to AV movement, while still making some stochastic deviations from AV-identical trajectories. These deviations are quantified using experimental data from human drivers in a driving simulator merged into a stochastic vehicle movement model. Experimental results suggest that, with this paradigm, AIM can decrease delay significantly, even at low AV penetration levels (less than 20%). Finally, I conceptualize intersection priority auctions into the newly developed AIM framework as itself a modular framework that supports the dispatch of multiple vehicles simultaneously from either separate lanes or a single lane without relying on preset signal phases. This auction framework further supports three payment formulas for the winner of the priority auction: first-price, second-price, or a novel externality payment mechanism. Using experiments implemented in the novel AIM simulator, my results demonstrate significant reduction in value-weighted delay using the multiple dispatch configuration and novel payment mechanism compared to other configurations, with the novel formula incentivizing truthful reporting of valuations more than its alternatives.
A strategic prioritization approach to airline scheduling during disruptions
(2015-05) Srivastava, Prateek Raj; Dimitrov, Nedialko B.; Fearing, Douglas
Air disruption scenarios due to inclement weather or air traffic congestion can result in significant imbalances in the demands and capacities of the affected airports. The Federal Aviation Administration (FAA) resolves these imbalances by implementing the Ground Delay Programs (GDP). In a GDP, the FAA first assigns new arrival slots to the airlines using the Ration By Schedule (RBS) approach, which is an allocation procedure that assigns slots to incoming flights based on a First-Scheduled, First-Served (FSFS) criterion. The FAA uses these new arrival slots to determine the expected delays and accommodates them as ground delays at departure airports. The notion of FSFS that forms the basis of RBS, is considered to be an industry standard of fairness. One of the major shortcomings of the RBS approach is that it does not distinguish flights based on factors like aircraft size, number of passengers, future aircraft schedules, etc. This results in an inefficient utilization of the airport capacities. To address this concern, Fearing and Kash proposed a two-stage, non-monetary strategic prioritization game in which airlines could participate and bid for priorities at different airports by taking into account their internal costs. This approach has several advantages over different market-based mechanisms like slot auctions, congestion pricing and slot exchanges. In this thesis, therefore, we develop their approach further both mathematically as well as empirically. Specifically, we prove that a pure strategy Nash equilibrium exists in the second stage of the game for the general multiple airlines and multiple airports case. In addition, by imposing the diagonal strict concavity conditions on the airlines' payoffs, we show that this pure strategy Nash equilibrium is unique for the two-airlines case. Our experimental simulations on historical data further show that this approach can achieve significant congestion cost benefits in comparison to the current RBS procedure.