Photovoltaic (PV) power plants face revenue risks in electricity spot markets due to three critical factors: solar power forecast errors

penalty mechanisms stipulated by China's ‘Dual Detailed Rules’ for deviation settlement

and electricity price volatility. To address these challenges

this study proposes a dynamic bidding strategy that systematically integrates solar power forecasts with penalty-aware optimization and price risk management. The strategy utilizes three key operational inputs: 1) solar power forecasts

2) day-ahead (DA) and real-time (RT) electricity prices

and 3) penalty thresholds defined in the ‘Dual Detailed Rules’. Our methodology establishes a decision framework where hourly bids are optimized through a profit-maximization model that balances energy revenue against penalty risks. Compared to conventional bidding methods

the proposed strategy achieves a 28.1% revenue increase during a certain hour by strategically controlling declared generation. Furthermore

it yields a 19% higher revenue during peak fluctuation periods

demonstrating its applicability across all trading intervals. The core theoretical contribution lies in bridging operational forecasting with market rule compliance—transforming raw prediction data into penalty-adjusted bids through deterministic optimization. This approach provides a systematic framework for PV plants to effectively convert forecasting capabilities into enhanced financial performance under China's specific electricity market regulations.