This paper proposes a snow ablation optimizer (SAO) for the dynamic dispatch of power and natural gas assimilating solar photovoltaic plants, wind generators, pumped hydro energy storage, and plug-in electric vehicle parking lots with charging and discharging facilities considering carbon capture with and without a demand response program. Power-to-gas technology consists of a carbon capture unit, an electrolyzer, a hydrogen storage unit, and a methanation process, which is used to supply the natural gas demand using CO2 acquired from thermal generating units and hydrogen generated by the electrolyzer. The SAO is a physics-based technique that imitates the sublimation and melting behavior of snow. The SAO comprehends a trade-off between exploitation and exploration in the solution space and avoids early convergence. The numerical results obtained from the suggested SAO are matched with the results obtained from the self-organizing hierarchical-particle-swarm-optimizer with time-varying acceleration coefficients and differential evolution. The obtained results showed that SAO’s minimum cost is less than that of HPSO-TVAC and DE in both Cases 1 and 2. In comparison to DE and HPSO-TVAC, SAO converges more quickly. The best minimum cost is reached by SAO before 150 iterations; however, HPSO-TVAC and DE require more than 150 iterations. From the numerical results, it is perceived that the minimum price acquired with the demand response program is less than that without it.