In the current work, a model has been developed using MATLAB along with artificial neural network (ANN) to optimally configure a hybrid renewable energy plant. This enabled to use a group of professional software for the design of the plant, and meanwhile conduct an optimization study for the plant configuration. The proposed plant is capable of producing a steady output to the grid at the least Levelized Cost of Electricity (LCOE), with minimum Loss of Power Supply Probability (LPSP) and excess energy. The plant consists of a wind farm, a solar PV plant, and a storage section containing Vanadium Redox Flow Batteries (VRFB) and hydrogen generation and storage system for running a Fuel Cell. Ras Ghareb site in Egypt is selected to prove the concept of the hybrid plant based on its favoured solar and wind resources and their complementarity. It has been found that the optimized hybrid combination of a baseload plant of an accredited capacity 200 MW will have 89 % of its output of electricity from direct renewable resources (Wind and PV), 8 % from FC, and 3 % from VRFB. The economic indicators show that the LCOE will be 0.0897$/kWh, at Zero LPSP and Zero Excess energy above the accredited capacity. Relaxing the LPSP by 1 % reduces the system total capital expenditure (CAPEX), and hence, the LCOE by 18.6 % down to 0.073 $/kWh. Seasonal storage has a great effect on the system's steady output and reliability, as both wind and PV potential are less in winter at the selected site. In addition, seasonal energy storage is the major cost driver in the hybrid system, causing baseload generation cost to exceed the conventional thermal baseload units, despite being cheaper than concentrated solar power (CSP). Hydrogen storage is favoured over batteries for seasonal storage due to its advantage for long term storage at relatively lower specific cost.