Graduation Year

2005

Document Type

Thesis

Degree

M.S.I.E.

Degree Granting Department

Industrial Engineering

Major Professor

Dr. Tapas K. Das.

Keywords

First price uniform auction, Second price uniform auction, Discriminatory auction, Multi-unit auctions, Reinforcement learning

Abstract

In a deregulated electricity market, auction serves as a primary pricing tool in various segments of the market including day-ahead, real time, ancillary services markets, and Financial Transmission Rights (FTRs) market. Deregulated power markets around the world use different auction strategies that exist in the literature, since very little comparative guidelines exist as to the relative merits of these strategies. In this thesis, a computational methodology and its solution framework are developed to evaluate the impact of an auction strategy on the equilibrium prices in a constrained network with multiple generators at nodes, and where transactions are settled using the optimal power flow (OPF) program. The methodology is tested on a power market represented by a sample 12-bus IEEE network available in the MATPOWER software, which is reconfigured to allow multiple generators to supply power at a bus.

The network is used as a platform to comprehensively assess the performance of uniform price auction, discriminatory auction, and second-price uniform auction. Auction rules are used to update generator costs, which are then introduced into the OPF program for obtaining optimal price and quantity allocations. This Auction-OPF procedure is embedded within a game theoretic model that obtains the equilibrium bidding strategies and the corresponding prices and quantities for the network. A detailed comparison of the auction mechanisms is carried out using different measures of performance such as revenue, average prices, and quantity weighted average prices. The comparison shows that there is, perhaps, an appreciable difference among the auction mechanisms.

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