Grasshopper Optimization Algorithm on Combined Economic Emission Dispatch Problem Involving Cubic Functions
Karthikeyan. R1, Subramanian. S2, Elanchezhian. E. B3

1Karthikeyan. R*, Lecturer Assistant Professor Deputed from Annamalai University, Alagappa Government Polytechnic College, Karaikudi, India.
2Subramanian. S, Professor, Annamalai University, Annamalainagar, India.
3Elanchezhian. E. B, Lecturer Assistant Professor Deputed from Annamalai University, Government Polytechnic College, Srirangam, India.
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 808-817 | Volume-9 Issue-1, October 2019 | Retrieval Number: A9337109119/2019©BEIESP | DOI: 10.35940/ijeat.A9337.109119
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (

Abstract: In this paper, grasshopper optimization algorithm is presented to resolve the combined economic emission dispatch (CEED) problem involving cubic functions considering power flow constraints. Electric power system wants to satisfy its customers load demand with minimum fuel cost and emission. Fuel cost and emission has instantly association with energy cost. In CEED problem, the price penalty factor occupies a cardinal role to fetch the optimal results. The various types of price penalty factor available in the literature are analyzed to determine the optimal one for the test cases considered. The test systems used in this CEED problem are 3 unit system considering transmission loss and 13 unit system considering valve point effects. The leading requirement in both the test cases is to optimize the total cost, fuel cost and emission. The numerical and statistical results affirm the high degree of the solution founded by GOA and its superiority is compared with already existing algorithms employed in solving CEED problems.
Keywords: CEED, Emission, Fuel cost, Grasshopper Optimization Algorithm, Price penalty factor, Total cost.