  This paper applies Benders decomposition to two-stage stochastic problems for
energy planning under climate uncertainty, a key problem for the design of
renewable energy systems. To improve performance, we adapt various refinements
for Benders decomposition to the problem's characteristics -- a simple
continuous master-problem, and few but large sub-problems. The primary focus is
stabilization, specifically comparing established bundle methods to a quadratic
trust-region approach for continuous problems. An extensive computational
comparison shows that all stabilization methods can significantly reduce
computation time. However, the quadratic trust-region and the non-quadratic
box-step method are the most robust and straightforward to implement. When
parallelized, the introduced algorithm outperforms the vanilla version of
Benders decomposition by a factor of 100. In contrast to off-the-shelf solvers,
computation time remains constant when the number of scenarios increases. In
conclusion, the algorithm enables robust planning of renewable energy systems
with a large number of climatic years. Beyond climate uncertainty, it can make
an extensive range of other analyses in energy planning computationally
tractable, for instance, endogenous learning and modeling to generate
alternatives.
