Differential evolution (DE) is one of the efficient evolutionary computing techniques that seem to be effective to handle optimization problems in many practical applications. Conversely, the performance of DE is not always flawless to guarantee fast convergence to the global optimum. It can certainly get inaction resulting in low accuracy of acquired results. An enhanced differential evolution (EDE) algorithm by integrating excited arbitrary confined search (EACS) to augment the performance of a basic DE algorithm have been proposed in this paper. EACS is a local search method that is excited to swap the present solution by a superior candidate in the neighbourhood. Only a small subset of arbitrarily selected variables is used in each step of the local exploration for randomly deciding the subsequent provisional solution. The proposed EDE has been tested in standard IEEE 30 bus test system. The simulation results show clearly about the better performance of the proposed algorithm in reducing the real power loss with control variables within the limits.