optimization of mixed-integer non-linear electricity generation expansion planning problem based on newly improved gravitational search algorithm

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.