A new Metaheuristic Method for Emission and Cost generation Reduction considering Stochastic Solar Power

The production of clean power and its incorporation into the grid is a major challenge, given energy variations and the instability of primary sources. This paper employed a new metaheuristic algorithm, the Red-Tailed Hawk (RTH) algorithm, designed to solve the Optimal Power Flow (OPF) problem while incorporating stochastic solar power. The RTH algorithm was tested on a modified IEEE 30-bus system, addressing a multi-objective function that combines total generation cost and the emission effect. The total generation cost includes both thermal and solar generation costs. Costs related to stochastic solar power generation include direct costs, reserve costs, and penalties for overestimating or underestimating available solar power. The results demonstrate that the RTH algorithm outperforms other methods, such as the Gold Rush Optimizer (GRO), Electric Eel Foraging Optimization (EEFO), and Dandelion Optimizer (DO). The RTH algorithm achieves a total generation cost of 867.5241 ${\$}$/h and an emission value of 1.32119 ton/h, showing superior performance in reducing emissions, offering faster convergence, and greater robustness compared to recent metaheuristic approaches. Additionally, when applied to the IEEE 30-bus system without solar integration, the emission value was 1.4401 ton/h. This represents an 8.26% reduction in emissions due to the integration of solar power, highlighting the environmental benefits of incorporating renewable energy sources.