Experimentation with Benders decomposition for solving the two-timescale stochastic generation capacity expansion problem

The main purpose of solving a classical generation capacity expansion problem is to ensure that, in the medium- to long-term time frame, the electric utility has enough capacity available to reliably satisfy the demand for electricity from its customers. However, the ability to operate the newly built power plants also has to be considered. Operation of these plants could be curtailed by fuel availability, environmental constraints, or intermittency of renewable generation. This suggests that when generation capacity expansion problems are solved, along with the yearly timescale necessary to capture the long-term effect of the decisions, it is necessary to include a timescale granular enough to represent operations of generators with a credible fidelity. Additionally, given that the time horizon for a capacity expansion model is long, stochastic modeling of key parameters may generate more insightful, realistic, and judicious results. In the current model, we allow the demand for electricity and natural gas to behave stochastically. Together with the dual timescales, the randomness results in a large problem that is challenging to solve. In this paper, we experiment with synergistically combining elements of several methods that are, for the most part, based on Benders decomposition and construct an algorithm which allows us to find near-optimal solutions to the problem with reasonable run times.