Agent-based Modeling and Simulation of an Optimal Bidding Approach for Financial Transmission Rights Auction

Abstract

For any participant of electricity market, bidding for financial transmission rights is an investment decision which requires evaluation of risk-return trade-offs. A market participant who is investing in financial transmission rights seeks to determine optimal bidding prices that maximize return as well as minimize associated risks. A new method is developed in this thesis to optimize bidding prices of a participant in financial transmission rights auction. The method also considers any auction revenue rights held by participant which enables him to hedge against uncertain expenses of buying the rights at auction clearing prices. This thesis provides complete mathematical model for the bidding based on mean-variance-skewness-kurtosis framework. It shows the comparison between estimate bidding approach and optimal bidding method, and shows that, compared to an estimate bidding approach, use of the optimal bidding method by all participants reduces their costs of acquiring the transmission rights.