Cost-Effective Unit Commitment of Thermal Units Considering Plug-in Electric Vehicles and Renewable Energy Sources using Chromatic Seahorse Optimizer

Abstract

This study proposes a novel and cost-effective approach to solving the Unit Commitment Problem (UCP) of thermal power systems by employing the Chromatic Seahorse Optimizer (CSHO) algorithm. The primary objective is to minimize the total operating cost while ensuring optimal scheduling of thermal generating units under dynamic smart grid scenarios. The rapid integration of Plug-in Electric Vehicles (PEVs)andRenewable Energy Sources (RES), particularly wind energy, adds uncertainty and complexity to power system operations, necessitating advanced optimization strategies.

The CSHO algorithmis a newly developed metaheuristic inspired by the spiral reproductive behavior of seahorses, enhanced through chromatic adaptation strategies to balance exploration and exploitation more effectively. This hybrid nature makes CSHO highly suitable for handling the nonlinear, constrained, and combinatorial characteristics of the UCP. The algorithm is implemented in MATLAB 2021, and simulations are conducted on the IEEE-39 bus system, modified to include equivalent PEVs and wind generation units.Results show that CSHO consistently achieves lower total operating costs across all scenarios compared to several state-of-the-art soft computing methods. The algorithm also demonstrates superior performance in terms of convergence speed and solution stability.