The following is a list of possible future studies that tangent this chapter:
• The possibility of uncertainty handling in hybrid modes (when different types of uncertainties exist in problem) should be investigated.
• With the deregulation of the power system industry, the uncertainties of renewable energies are not limited to their power generation output. The decisions of the private sector regarding the investment are also subject to uncertainty.
• The introduced stochastic uncertainty handling tools provide the possibility of tradeoff for the decision maker between the accuracy and the high computational performance.
• With the development of smart grid concepts, methods that will provide accuracy with low computational burden have to be developed. This would make the decision maker enable to use them in real-time applications.
The taxonomy of uncertainty modeling of renewable energies is introduced in this chapter. Three main stochastic methods including Monte Carlo simulation, point estimate method, and scenario-based modeling are analyzed and implemented. The simulation results showed that although Monte Carlo simulation is inherently an iterative approximate model for handling the stochastic uncertainties, it is more accurate than others. On the other hand, the computational burden of this method is higher than the rest of them. This chapter also deals with situations where both stochastic and possibilistic uncertainties exist in the problem. The applicability of each model is demonstrated by applying it on a simple two-bus network.
Acknowledgments To my lovely, beautiful, and faithful wife, Soudeh Ziapour Razlighi. She always comforts and consoles, never complains or interferes, asks nothing, and endures all.