Optimal Power Flow Control in the Iraqi Power Grid Using Artificial Intelligence Algorithms for Carbon Emission Reduction

Iraq’s power sector remains in a protracted and severe crisis characterized by a significant mismatch between supply and demand, high levels of losses during transmission and distribution, as well as an increasing challenge to the resources base being largely unfavourably. These inefficiencies impose heavy costs on the national economy in excess of 40 billion annually and they also reinforce greenhouse gas emissions, and exacerbate environmental issues. To deal with these problems, we proposed in this paper that a new hybrid AI tool should be developed for the solution of multi-objective optimization problem based on purpose Genetic Algorithm (GA) merger with Particle Swarm Optimization (PSO) to solve the multi-objective Optimal Power Flow model as a case of Iraqi system. Optimization aims to achieve two main goals at the same time: lowering the total cost of generation and cutting carbon emissions, all while meeting the system's operating points and limits. The approach is executed via simulations on a representative model of the Iraqi 400 kV high-voltage network. The study also uses official reports from the Iraqi Ministry of Electricity and the World Bank, as well as good academic support. The results indicate that the developed GA-PSO hybrid method outperforms certain traditional optimization techniques and single-algorithm strategies in terms of advantages such as reduced generation costs, decreased system losses, lower carbon emissions, and enhanced voltage stability across the grid. These studies fundamentally offer intuitive evidence-based methodologies for the enhancement of the Iraqi power sector in an efficient and sustainable manner. This illustrates one viable approach towards a more secure, affordable, and environmentally friendly energy future for the nation.