Newly Developed Enhanced Imperialistic Competitive Algorithm for Design Optimization of an Autonomous Hybrid Green Power System

The importance of economics for owning hybrid green power systems (HGPS) warrants development of optimization methodologies with more effective search capabilities for determination of global minimum for costs. The objective of this study is to present several newly developed enhancements for imperialistic competitive algorithm (ICA) for design optimization of an autonomous HGPS with considerations for economics and reliability. HGPS examined consists of photovoltaic (PV) modules in a panel, wind turbines (WT), and storage batteries (SB). Utilizing an IEEE load profile and actual solar irradiation and wind speed data, the economics is evaluated based on annualized cost of system (ACS) and reliability constraint specified in terms of loss of power supply probability (LPSP). The simulation results show that enhanced ICA (EICA) developed in this study has a better convergence rate, as compared with other population based optimization methods such as ICA and genetic algorithm (GA). It is found that the new enhancements developed for EICA result in lower computation time for determining the optimal configuration of HGPS equipment by 40 and 79 % for ICA and GA, respectively. For LPSP of 2 %, it is determined that EICA results in lower ACS by 11.60 and 6 % in comparison with ICA and GA, respectively. For computation time, convergence occurs in 33, 55, and 160 minutes for EICA, ICA, and GA algorithms, respectively.