University of Isfahan Computational Intelligence in Electrical Engineering 2821-0689 2 1 2011 04 21 Generation Expansion Planning by a Modified SFL Algorithm Generation Expansion Planning by a Modified SFL Algorithm 27 44 15308 FA Morteza Jadidoleslam 1Dept. of Electrical Engineering, Isfahan University of Technology, Isfahan, Iran Ahsan Bijami 2Dept. of Electrical Engineering, Isfahan University of Technology, Isfahan, Iran Akbar Ebrahimi 3Dept. of Electrical Engineering, Isfahan University of Technology, Isfahan, Iran Journal Article 2016 06 14 In this paper, Generation Expansion Planning (GEP), is modeled as an optimization problem in which the objective function is to minimize the total investment, operation, and outage (energy not served) costs of power system as well as salvage value of investment costs. Generation system reliability is assessed and provided by means of EENS and LOLP indices. To solve the GEP problem, a new Modified Shuffled Frog Leaping namely MSFL algorithm is proposed. A new frog leaping rule and a new strategy for frog distribution into memeplexes is introduced to improve the local exploration and performance of the original SFL algorithm. To show the effectiveness of the MSFL algorithm, it is applied to a test system with 15 existing power plants and 5 types of new candidates, for a 12-years and a 24-years planning horizon. The original SFL algorithm and the Genetic Algorithm (GA) are also applied to solve the GEP problem. Simulation results show the advantages of the proposed MSFL algorithm over the original SFL and GA. In this paper, Generation Expansion Planning (GEP), is modeled as an optimization problem in which the objective function is to minimize the total investment, operation, and outage (energy not served) costs of power system as well as salvage value of investment costs. Generation system reliability is assessed and provided by means of EENS and LOLP indices. To solve the GEP problem, a new Modified Shuffled Frog Leaping namely MSFL algorithm is proposed. A new frog leaping rule and a new strategy for frog distribution into memeplexes is introduced to improve the local exploration and performance of the original SFL algorithm. To show the effectiveness of the MSFL algorithm, it is applied to a test system with 15 existing power plants and 5 types of new candidates, for a 12-years and a 24-years planning horizon. The original SFL algorithm and the Genetic Algorithm (GA) are also applied to solve the GEP problem. Simulation results show the advantages of the proposed MSFL algorithm over the original SFL and GA. https://isee.ui.ac.ir/article_15308_d9d2e2201a3accc8cfd508c0c8a98ae8.pdf