A Data-Driven Pattern Extraction Method for Analyzing Bidding Behaviors in Power Markets

Myriad studies have been conducted on bidding behaviors following a worldwide restructuring of the electric power market. The common theme in such studies involves idealized and theoretical economic assumptions. However, practical bidding behavior could deviate from that based on theoretical assumptions, which would undoubtedly limit the effectiveness and practicality of the prevalent market-based studies. To analyze the actual bidding behavior in power markets, this paper proposes a data-driven analysis framework for bidding behavior in which a data standardization processing method is proposed that addresses the particularities of the bidding data and provides a fundamental dataset for further market analyses. Then, an adaptive clustering method for bidding behavior is developed that applies the ${K}$ -medoids method and the Wasserstein distance measurement to extract the generators’ bidding patterns from a massive dataset. An empirical analysis of the bidding behavior is conducted on actual data from the Australian energy market. The typical bidding patterns are extracted, and the bidding behaviors are further analyzed.