Renewable energy sources, such as solar and wind, play vital role in reducing emissions of carbon dioxide and combating climate change. However, their stochastic behavior has an impact on modern electric networks. Hence, Modelling variability and uncertainty in optimal power flow (OPF) concerns is critical for ensuring dependable and environmentally friendly grid operations. This study investigates the impact of climate change on the integration of wind and solar energy into power systems, with a specific focus on how temperature variations affect the performance and optimal dispatch of wind turbines and solar photovoltaic (PV) systems. To minimize total costs and carbon emissions, both single-objective and multi-objective optimization problems are developed, incorporating temperature-dependent de-rating effects on PV modules and wind turbines. Mayfly algorithm (MA) is applied to the IEEE-30 bus system for Optimal Power Flow (OPF), achieving a 0.6% and 0.5% reduction in fuel cost and carbon emissions compared to PSO and further techniques in single-objective OPF. The study extends to Stochastic OPF (SOPF) on a modified IEEE-30 system with two wind farms and one PV plant. Where, temperature-dependent models are used for both wind energy and solar energy. To additionally encourage and increase the reliance on renewable energy, Carbon credit concept is added to the total cost objective function as novel contribution. The results show that when the carbon credit is taken into account the total cost is reduced by 0.8% compared to the case when it is not considered. To assess the climate change impact, it is found that at 40 °C, the total cost and emissions increase by 21% and 45.67% respectively when minimizing cost, and by 9.67% and 45.7% when minimizing emissions. The multi-period analysis evaluates the evolution of renewable penetration over the 25-year lifetime by considering the gradual reduction in renewable output caused by annual degradation and temperature-related derating. The renewable penetration limits are therefore updated across the planning period to reflect the reduced contribution of PV and wind generation over time. where, this impact is on total cost and carbon emissions over 25-year lifetime through both single and multi-objective dynamic Multi-Period SOPF (MPOPF) problem. For single-objective MPSOPF problem, after 25 years, the results at 40 °C show that the total cost and emissions increase by 24.96% and 51.8% respectively when minimizing the total cost compared to the results obtained at the beginning of wind and solar plants operation. Moreover, Multi-Objective SOPF is solved, a fuzzy-based Pareto front is used, the outcomes show that when the ambient temperature rises to 40 °C, the compromise solution shows an increase of 16.65% total cost and 41.87% carbon emission respectively. For Multi-Objective MPSOPF problem, at 40 °C, the compromise solution shows a 20.16% cost and 60.1% emission increase after 25 years compared to the case when the temperature rise and degradation effect are not taken into account. The findings reveal that climate change and degradation adversely affect renewable energy integration, resulting in increased reliance on thermal power and emphasizing the need for informed planning of sustainable energy infrastructures and re-powering the renewable energy resources at the end of their lifetime.