Optimization of Technical and Economical Objective Functions of Hybrid Renewable Energy Generation Based Genetic Algorithm - Novendra Setyawan; Zulfatman Zulfatman; Haris Rahmana Putra; Muhammad Ikhwanul Khair (Indonesian Journal of Artificial Intelligence and Data Mining, 2021)
This study is aimed to optimize the technical and economic objective functions of a renewable energy hybrid generator system by using genetic algorithms (GA) in order to create a balanced and optimal power generation system configuration. The technical and economic aspects used were the Loss of Power Supply Probability (LPSP) and Annualized Cost of System (ACS), respectively. The objective functions of GA method were LPSP and ACS. The types of power plants used in this hybrid system were photovoltaic (PV), Wind Turbine (WT), battery, and Micro Hydro Power Plant (MHPP). Validation on the GA method was done by simulation in Matlab. Results of the simulation show that the use of the GA offers the most balanced system configuration with less expensive costs and a very good level of system reliability against hybrid systems. The use of the objective function with penalty factor scenario in GA is not as effective as the conventional GA, following the weakness of its evaluation results.