Optimal techno-economic design of hybrid PV/wind system comprising battery energy storage: Case study for a remote area

•The GWO for optimal design of autonomous PV/Wind/Batteries hybrid microgrid is addressed for a remote area.•The objective is to cover load profile with different LPSP levels while minimizing the COE.•COE of the obtained configuration is compared to the results of PSO, GA, WHO and the commercial HOMER software.•The suggested configuration is verified and tested to cover hourly load requirements. Due to the sustainability and emission-free property of hybrid renewable energy sources (RESs), they became challenging alternative sources to conventional energy production facilities. However, cost of energy (COE) and the intermittent nature of RESs are two adverse factors complicating the problem of system appropriate sizing. This study develops a generalized mathematical model to find the optimal PV/wind/battery system sizes for remote areas. The proposed methodology is applied on a remote area in Egypt-Sinai called Ras-Shaitan. The purpose of the optimization process is to meet the load demand while minimizing the COE under different loss of power supply probability (LPSP). The grey wolf optimizer (GWO) is employed to decide the number of units among photovoltaic, wind and battery banks to achieve the best minimum objective value. To validate the solution quality generated by the GWO, the widely used HOMER software, the particle swarm optimizer (PSO) and genetic algorithm (GA) as a well-known meta-heuristic algorithms, are applied to the same and their results are compared with those obtained by the GWO. Further validations are made, in which, a new algorithm called wild horse optimizer (WHO) is also applied as a new algorithm and its results compared to aforementioned methods. Moreover, different solutions are offered according to the LPSP. The optimal system offered by HOMER has COE of 0.118 $/kWh, while GWO resulted in a save of about 17% of the COE. The GWO has a better convergence trend and best statistics measures than the PSO.