A stochastic policy algorithm for seasonal hydropower planning

Abstract

Hydropower producers need to plan several months or years ahead to estimate the opportunity value of water stored in their reservoirs. The resulting large-scale optimization problem is computationally intensive, and model simplifications are often needed to allow for efficient solving. Alternatively, one can look for near-optimal policies using heuristics that can tackle non-convexities in the production function and a wide range of modelling approaches for the price- and inflow dynamics. We undertake an extensive numerical comparison between the state-of-the-art algorithm stochastic dual dynamic programming (SDDP) and rolling forecast-based algorithms, including a novel algorithm that we develop in this paper. We name it Scenario-based Two-stage ReOptimization abbreviated as STRO. The numerical experiments are based on convex stochastic dynamic programs with discretized exogenous state space, which makes the SDDP algorithm applicable for comparisons. We demonstrate that our algorithm can handle inflow risk better than traditional forecast-based algorithms, by reducing the optimality gap from 2.5 to 1.3% compared to the SDDP bound.