Abstract: In the electricity spot market, electricity retailers face dual uncertainties that arise from market electricity prices and user loads. The day-ahead bidding process can incur additional purchasing costs owing to these uncertainties. However, existing stochastic optimization methods for electricity purchasing strategies and risk management, such as the conditional value at risk（CVaR）, suffer from problems related to equiprobable reduction in key scenarios and subjective confidence level selection. To address these challenges, this study introduces a scenario reduction method based on k-means and a confidence level optimization method based on extrapolation-interpolation, proposing an improved CVaR day-ahead bidding optimization model and its solution strategy based on the traditional neutral risk model and CVaR optimization model. The simulation results validate that the improved CVaR optimization model effectively reduces the overall purchasing costs and potential risk losses for the electricity retailer. This study explores the impact of the day-ahead bidding optimization strategy under different levels of risk aversion and market volatility, demonstrating the applicability and robustness of the improved optimization strategy.