The paper proposes four hybrid storage options: battery-supercapacitor, battery-flywheel, battery-pumped hydro storage, and battery-fuel cell. A probabilistic approach based on the artificial neural network is implemented to cover the weather uncertainty. The developed options and the approach are combined with comprehensive eco-techno-economic optimization study. Multi-objective optimization scenarios are investigated to reduce either/both total cost or/and greenhouse gas (GHG) emissions considering Utopia point approach. Two new hybrid particle swarm optimization - grasshopper optimization algorithm (PSO-GOA) methods are developed: GOA updated PSO and GOA initialized PSO algorithms. The effectiveness of the developed optimizers and the Utopia point approach are validated. Sensitivity analysis is performed to demonstrate the variable parameters that majorly influence the investment costs and GHG emissions. Through numerical simulations via Matlab™, PV modules rating power, number of wind turbines, and the load demand are the key factors that impact the eco-techno-economic at Qusair city, Egypt. Through the Utopia point solution, supercapacitor integrated battery demonstrates enhancements by 42.48% and 12.92% in the GHG and levelized cost of energy (LCOE) compared to the economic and environmental solutions. The salvage market fosters the hybrid flywheel and pump hydro storage integrated battery options by reducing the LCOE by 44% and 51%, respectively.