Graduation Year

2004

Document Type

Thesis

Degree

M.S.I.E.

Degree Granting Department

Industrial Engineering

Major Professor

Tapas K. Das, Ph.D.

Committee Member

William A. Miller, Ph.D.

Committee Member

Jose L. Zayas-Castro, Ph.D.

Keywords

restructuring, market power, reinforcement learning, FTR, stochastic games

Abstract

Motivated by deregulation in major service sectors like airlines, banking and telecommunication, the electric industry is undergoing a major transformation. However due to design inefficiencies, restructuring of the power sector, so far, has not been a major success. A lack of comprehensive quantitative models has resulted in the inability of the market designers to evaluate market performance and develop successful market designs. A comprehensive model should include market features like two-settlement system, transmission congestion, financial transmission rights (FTRs), demand elasticity, demand-side bidding and other market rules.

The contribution of this thesis includes development of an exhaustive modeling framework that includes the above mentioned market features and also development of a computationally effective solution methodology. The market designers would use this methodology in the development of alternative conceptual market design frameworks, and also for assessing the impact of various market rules on market performance.

The noncooperative bidding behavior of the generators in both FTR and energy markets are modeled as nonzero-sum stochastic games. Since the bidding strategies in the FTR and energy games are dependent on each other and jointly impact the market performance, a two-tier learning approach is developed. Players (e.g. generators) first bid in the FTR market. FTR bids are then taken into account in the process of selecting bids in the energy market. The FTR bids and the energy bids together decide the market equilibrium and the resulting performance. This performance measure is then used to evaluate success of FTR bidding strategy. Several example power networks are studied to expose the modeling and learning based solution approach.

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