MT Schedule Stochastic Algorithm

Units:	-
Default Value:	0
Validation Rule:	In (0,1)
Description:	Algorithm invoked by the Stochastic Method when a scenario tree is present
Detail:

This setting determines which solution method is employed to solve a stochastic optimization in the presence of a scenario tree (as defined by a Global object). The available methods are:

• Rolling Horizon (value = 0, default): Solves using the Rolling Horizon solution method as described in the article Multi-stage Stochastic Optimization. This method is the most robust. It can handle any level of complexity including integer decision variables, non-linear constraints etc and guarantees a fully converged solution. In many cases this method is also the fastest to execute. However, if you require output of the Future Cost Function diagnostic this method will only produce the optimal cuts. Choose the SDDP method if you need the full set of Benders cuts.
• Stochastic Dual Dynamic Programming (SDDP) (value = 1): Solves using the 'traditional' SDDP algorithm. This method performs a series of forward and backward passes across the stages of the horizon. Benders cuts are developed at each stage representing the future cost function and value of storage. Iterations occur until convergence criteria are met. This method naturally results in a detailed Future Cost Function which can be used as input to another simulation e.g. to a short-term ST Schedule with the FCF defining the future value of storage.