Computational Complexity of Algorithms for Optimization of Multi-Hybrid Renewable Energy Systems

A well designed algorithm helps to determine the optimum size of power generation system. And for Multi-Hybrid Renewable Energy Systems (MHRESs), comprising of two alternative energy systems working together, such as PV array, Wind turbine, and Hydro generation capacity for back-up and grid integrated MHRES of desired load, a thoroughly designed algorithm will nevertheless assist in the optimal sizing of such a MHRES. In this paper, MHRES was discussed and computational complexity of algorithms was briefly analyzed. These computational complexities are including of: Complexity of converting among Context Free Grammars (CFGs) and Pushdown Automata (PDAs); Running time of conversion to Chomsky Normal Form (CNF); Testing emptiness of Context Free Languages (CFLs); Testing membership in a Context Free Language (CFL); and Complexity of Primality Testing. This was done to understand the ingenious idea behind computational complexity of algorithm. Thereby giving an understanding on how fast a program for optimizing MHRES will be when it performs computations and how a MHRES algorithm will behave as the input grows larger.