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 economicsof electrical energy generation for planning purposes. This study aims to examine the applicability ofGravitational Search Algorithm (GSA) and the newly improved GSA (IGSA) for optimization of themixed-integer non-linear electricity generation expansion planning (GEP) problem. The performanceindex of GEP problem is defined as the total cost (TC) based on the sum of costs for investment andmaintenance, unserved load, and salvage. In IGSA, the search space is sub-divided for escaping fromlocal minima and decreasing the computation time. Four different GEP case studies are considered toevaluate the performances of GSA and IGSA, and the results are compared with those from implementingparticle 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 withGSA, the simulation results show that IGSA requires less computation time, in all cases.