Starfish Optimization Algorithm for Economic Emission Dispatch with Chance Constraints and Wind Power Integration

Abstract

This paper demonstrates the application of the Starfish Optimization Algorithm (SFOA) to solve the Economic Emission Dispatch (EED) problem considering chance constraints, integrating wind energy sources. Wind power is modeled as a Weibull distribution, and the problem is set up in the framework of Chance-Constrained Programming (CCP) to capture the variability of wind energy. The applied chance constraints ensure that the power flow equation is satisfied with a specified probability, and the resulting CCP formulation is transformed into a deterministic optimization problem using the premises of probability theory. Inspired by the peculiar behaviours of starfish, such as exploration, preying, and regeneration, the SFOA inspires strong global search capability and convergence efficiency. This approach effectively minimizes both generation costs and emissions while addressing the uncertainty associated with wind power. The method is tested on a ten-unit power system with wind energy integration, showing the robustness and efficiency of SFOA in achieving optimal solutions for the chance-constrained EED problem.