Greenhouse gas emissions from the current means of power production are one of the leading contributors to global warming and climate change. The energy market must facilitate the rapid transition to low-carbon and renewable energy sources to replace carbon-emitting and non-renewable fossil fuel sources. However, the utilization of renewable energy sources introduces a new set of challenges in managing operations due to the randomness exhibited by uncontrollable sources. A mix of controllable and uncontrollable sources is required to serve as a buffer in case yield from variable sources becomes insufficient. The benefits of Hybrid Renewable Energy Systems (HRES) rely on the location of the system and the optimal use of energy sources available in the locality to satisfy demand loads at the lowest cost and environmental impact. This study proposes a target-oriented robust optimization model for scheduling the production and distribution of power through a HRES capturing uncertainties in energy source availabilities. An illustrative case study is solved to demonstrate the capabilities of the model. This model provides a portfolio of solutions depending on the risk appetite of the decision-maker. In particular, the results suggest the importance of properly managing the displacement of conventional sources with renewable energy sources, especially when hedging against significant supply and availability variability.