This paper focuses on investigating strategies for market bidding portfolios involving wind storage plants in electricity market transactions. It develops bidding portfolio models for both day-ahead and real-time markets, considering uncertainties in wind power output and market prices. Initially, the study employs the CNN-LSTM-ATT model to predict electricity prices using historical data, enhancing prediction accuracy significantly and offering a robust foundation for price forecasting. Furthermore, the paper introduces the CVaR model to evaluate risks encountered by power plants, thereby improving risk management and quantifying uncertainties crucially for the proposed bidding strategy through CVaR threshold computation. Finally, it constructs a bidding strategy integrating day-ahead and real-time markets, implementing a tailored approach based on comparative day-ahead and real-time tariff assessments to maximize revenue for wind storage power plants. Illustrating with real-time data from a wind farm in Jilin Province, the paper demonstrates the effectiveness and practical feasibility of the proposed strategy within the electricity market, underscoring its significant practical implications and potential for broader application.