Optimization of Mixed-Integer Non-Linear Electricity Generation Expansion Planning Problem Based on Newly Improved Gravitational Search Algorithm

Document Type : Research Article


Energy Research Center, Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran


Electricity demand is forecasted to double in 2035, and it is vital to address the economics
of electrical energy generation for planning purposes. This study aims to examine the applicability of
Gravitational Search Algorithm (GSA) and the newly improved GSA (IGSA) for optimization of the
mixed-integer non-linear electricity generation expansion planning (GEP) problem. The performance
index of GEP problem is defined as the total cost (TC) based on the sum of costs for investment and
maintenance, unserved load, and salvage. In IGSA, the search space is sub-divided for escaping from
local minima and decreasing the computation time. Four different GEP case studies are considered to
evaluate the performances of GSA and IGSA, and the results are compared with those from implementing
particle swarm optimization algorithm. It is found that IGSA results in lower TC by 7.01%, 4.08%,
11.00%, and 6.40%, in comparison with GSA, for the four case studies. Moreover, as compared with
GSA, the simulation results show that IGSA requires less computation time, in all cases.


Main Subjects

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