<p>The demand for electricity continues to rise, and the integration of renewable energy sources, particularly solar power, becomes increasingly important. However, the variable and unpredictable nature of solar energy creates challenges for power producers, especially when optimizing bids for grid integration. This paper introduces the hybrid particle swarm optimization-gravitational search algorithm (HPSO-GSA) as a novel method to maximize profits for solar power producers, despite the inherent variability of solar output. By exploring various optimization techniques, the research identifies HPSO-GSA as the most effective in boosting marginal profits, outperforming traditional algorithms such as the gravitational search algorithm (GSA), particle swarm optimization (PSO), and real-coded genetic algorithm (RCGA). This advanced optimization approach enables solar power producers to better navigate the complexities of energy markets, ultimately achieving higher profitability. Moreover, the paper underscores the importance of sustainability within energy systems, emphasizing the need for long-term viability in both production and distribution. The HPSO-GSA method not only enhances immediate financial returns but also contributes to the broader objective of sustainable energy development, ensuring that solar power remains a crucial component of the future energy landscape.</p>