Wind power and solar photovoltaic (PV) power are two of the most implemented renewable energy; however, they are vulnerable to weather uncertainties. Most existing works developed energy models by estimating the mean production, overlooking the risk that some undesired productions with nonnegligible probabilities could be greatly deviated from the mean. Moreover, current hybrid wind-solar energy models lack deliberation on the co-location interferences which spoil the production. Considering weather uncertainties and co-location interferences, a flexible and applicable hybrid energy model is demanded. This paper addresses these underexplored yet important issues. First, we apply Monte Carlo method for simulating uncertain wind, sky and dust conditions based on multi-year real-world data. Secondly, the wind-solar farm co-location interferences, such as wind-turbine's shade and penumbra on PV surfaces, forbidden area for maintenance roads, and land-usage conflict, are deliberately modeled. Thirdly, we propose five risk-aware energy optimization (RAEO) models which provide various metrics for evaluating production benefit, cost, and risk. The experimental results show that our RAEO models can deal with weather uncertainties and co-location interferences and are flexible enough to provide various forms of analytics as responses to decision-maker's needs. (C) 2020 Elsevier Ltd. All rights reserved.