Scientific workflows require significant computational power, resulting in considerable energy consumption and carbon emissions. While traditional scheduling techniques reduce energy usage through machine heterogeneity, they remain constrained by hardware attributes. Renewable energy sources are an alternative to minimize environmental impact. Solar energy, though intermittent, creates temporal energy heterogeneity that can be leveraged even across homogeneous machines. However, the intermittency may lead to task delay, increasing the workflow’s finish time (makespan), a key user concern. We propose a renewable energy-aware scheduling algorithm to minimize non-renewable energy usage and makespan. The user must provide the algorithm with a deadline, which the algorithm uses to delay the tasks to use more renewable energy. Our evaluation using real and synthetic workflows demonstrates that, depending on the user’s flexibility, the usage of brown energy can be reduced drastically. The algorithm can save 99.98% of non-renewable energy with a makespan increase of 137.37%. Under limited renewable energy availability, non-renewable energy usage is still reduced by 12.50%, with only a 10.28% increase in makespan.