Puma algorithm for environmental emissions and generation costs minimization dispatch in power systems

Optimizing power systems is a crucial task nowadays for cost-effective operations that ensure profitability for providers and affordability for consumers. Also, efforts are directed toward harmonizing economic objectives with environmental conservation through accurate modelling of the Economic and Environmental Dispatch (EED) problem. This study adopts a more complex approach by formulating the fuel cost and produced emission objectives as cubic polynomial functions. Also, this paper proposes a novel Puma Optimization Operator (POO), inspired by the hunting behavior of pumas for the optimal allocation of power generation across various units, considering both economic and environmental factors. It efficiently navigates the solution space by balancing exploration and exploitation, leveraging puma-like intelligence to minimize both fuel costs and greenhouse gas emissions, including CO2, NOx, and SO2. The POO algorithm is tested on the IEEE 30-bus power system with six thermal units, delivering superior performance compared to advanced optimization algorithms such as the Osprey Optimization Algorithm (OOA), Aquila Optimizer (AO), Slim Mould Algorithm (SMA), Artificial Rabbit Optimization (ARO), and Coati optimization technique. The POO algorithm consistently outperforms other algorithms in minimizing both generation and emission costs across all loading levels, with improvement percentages ranging from approximately 1.221 % to 1.6 % compared to OOA, 0.59 % to 0.86 % compared to AO, 2.47 % to 3.42 % compared to SMA, 0.89 % to 1.67 % compared to Coati and 0.03 % to 0.13 % compared to ARO. Moreover, statistical analysis underscores POO's superior performance compared to other optimizers, establishing it as a highly competitive option concerning both cost and emissions. Also, the study is extended to address the dynamic ED problem, incorporating a 24 h load demand profile and ramp-rate constraints for power generation units. The proposed POO dynamically adjusts the power outputs of generators to meet hourly demand while adhering to operational limits. The results demonstrate that the proposed approach ensures smooth transitions in generator outputs, respects dynamic operational constraints, and achieves a realistic balance between cost efficiency and environmental impact.