Abstract
The objective of this research is to investigate the impact of different pricing rules on the economic performance of a deregulated electricity market. In particular, the influence on the bid prices and profits of generators, total dispatch cost, and the volatility of these values will be examined. Given the debate, over the past two decades, regarding the selection of the best pricing rules, the applicability of the Revenue Equivalence Theorem in the deregulated electricity market is revisited in this research. This theorem has been adopted in the literature as a theoretical support for deciding pricing rule in the market settlement process. In this research, it is hypothesized that the Revenue Equivalence Theorem may not hold. Therefore, it is appropriate to analyze market performance when different pricing rules are imposed to govern the market. Furthermore, this research also highlights the importance of generators bidding strategies in the competitive electricity market. The different methods used to design generators bidding strategies as presented in the literature have been reviewed. In order to investigate the different impact of pricing rules on market performance, this research employs a computer simulation method. A simulation platform is used to imitate the business activities in the electricity auction market, mainly in relation to the bidding, scheduling and dispatching processes. An agent-based approach is adopted for this purpose. The competing generators in the electricity market are modeled as agents. Bid stacking is used to model the optimization process of the Independent System Operator. The generator agents bidding strategies are designed using the Q-Learning algorithm. A Q-Learning-driven generator agent enables it to actively learn from the historical and market information leading to more realistic and practical results. Different pricing rules under both maximum quantity bidding and variable quantity bidding are studied separately. The influence on the bid prices and profits of generators, total dispatch cost, and the volatility of these values are analyzed accordingly.
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