  Convergence bidding, a.k.a., virtual bidding, has been widely adopted in
wholesale electricity markets in recent years. It provides opportunities for
market participants to arbitrage on the difference between the day-ahead market
locational marginal prices and the real-time market locational marginal prices.
Given the fact that convergence bids (CBs) have a significant impact on the
operation of electricity markets, it is important to understand how market
participants strategically select their CBs in real-world. We address this open
problem with focus on the electricity market that is operated by the California
ISO. In this regard, we use the publicly available electricity market data to
learn, characterize, and evaluate different types of convergence bidding
strategies that are currently used by market participants. Our analysis
includes developing a data-driven reverse engineering method that we apply to
three years of real-world data. Our analysis involves feature selection and
density-based data clustering. It results in identifying three main clusters of
CB strategies in the California ISO market. Different characteristics and the
performance of each cluster of strategies are analyzed. Interestingly, we
unmask a common real-world strategy that does not match any of the existing
strategic convergence bidding methods in the literature. Next, we build upon
the lessons learned from the existing real-world strategies to propose a new CB
strategy that can significantly outperform them. Our analysis includes
developing a new strategy for convergence bidding. The new strategy has three
steps: net profit maximization by capturing price spikes, dynamic node
labeling, and strategy selection algorithm. We show through case studies that
the annual net profit for the most lucrative market participants can increase
by over 40% if the proposed convergence bidding strategy is used.
