Optimal economic model of a combined renewable energy system utilizing Modified

In this paper, an optimal solution has been designed for a combined photovoltaic (PV), fuel cell (FC), and wind system. The idea is to manipulate renewable sources to provide clean energy. The main load demand here is supplied by the grid system, and the role of the proposed system is a backup unit. The proposed system has been used to feed the load into a marketplace in Amman Beach, Jordan. The study here aims to the optimal selection of the size of the renewable source to provide minimum total net present cost and to provide a definite number of the drop of energy supply possibility. This study then utilized an improved Metaheuristics algorithm, called Modified Snake Optimization Algorithm to provide an optimization process. A comparison is conducted among the final results of the method and two other methods, including CHS/FA (combined Harmony Search/Firefly algorithm) and PSO (particle swarm optimizer). Simulations are performed assuming a 5.11 MWh average daily load in the research area, 400 kW peak load, and 0.497 loading factor. Simulations show 2 % optimal sizing results for the loss of power supply probability using the proposed MSO algorithm. Changing the grid accessibility ratio from ninety percent (base case) to fifty percent resulted in a growth in the LPSP amount that varies from 3.5 % to 23.5 %. Further, the results show that the cost of energy for the proposed MSO with 0.0461 $/kWh is less than the PSO and CHS/FA. Also, results show better convergence values in 18 iterations than the other algorithms. Experiments showed good efficiency than other comparative methods when it comes to convergence time, system size, and cost of production. Finally, the implementation of the proposed algorithm to optimize the studied hybrid system can achieve superior and competitive results; (e.g, achieving a lower energy cost compared to PSO and CHS/FA algorithms).