| The spatial mismatch between China's energy resource endowment and demand, coupled with the rapid development of clean energy, has led to prominent issues of wind, solar, and hydropower curtailment, necessitating power market reforms to optimize resource allocation. The inter-provincial power spot market promotes large-scale renewable energy integration and power surplus-deficit balancing through a "unified market, two-level operation" system. However, the current market price cap mechanism is imperfect, with static price limits struggling to balance the dynamic needs of both supply and demand sides and the goals of ensuring supply and stabilizing prices. Analysis of bidding behaviors of power plants and electricity purchasers, as well as feature extraction for diverse market participants, remains insufficient. Based on trial operation data from the 2022 inter-provincial spot market, this study employs a big data-driven approach to systematically analyze the bidding characteristics and clearing outcomes of thermal power, hydropower, wind power, solar power, and electricity purchasers in counterpart provinces, revealing their temporal and seasonal behavioral patterns. Furthermore, the K-Means algorithm is applied to classify the bidding behaviors of different unit types, extracting key differences in duration, bid volume, and bid price to facilitate the analysis of influencing factors on the bidding behaviors of various unit types and electricity purchasers in counterpart provinces. |