To address the issue of renewable energy consumption responsibilities for electricity sales companies, a power purchase and sale strategy that considers both user demand response and renewable energy consumption weight is proposed. Firstly, a bilevel optimization model is constructed, incorporating user demand response and renewable energy consumption weight. The upper-level model sets consumption weight for different time periods based on the characteristics of wind and solar power output, and establishes a time-segmented power purchase and sale decision model with the goal of maximizing the electricity sales companies’revenue. The lower-level model involves users shifting and reducing their loads according to the real-time prices set by the electricity sales companies, with the objective of minimizing the users’overall electricity cost, thereby creating an optimized energy usage model for users. Secondly, the luminance attraction mechanism of the firefly algorithm is used to improve the multi-objective particle swarm optimization algorithm, and the improved algorithm is used to solve the double-layer optimization model. Finally, the effectiveness and superiority of the improved algorithm and strategy proposed are verified through a case study analysis.