Considering short-term variability in long-term hydropower scheduling by multi-stage Benders decomposition and residual demand functions

Abstract Balancing technical details and computational complexity is an important trade-off in long-term hydropower scheduling (LTHS) models. Rapidly increasing shares of variable renewable energy sources (VRES) challenges historical assumptions regarding the appropriate level of technical details and the representation of uncertainties in LTHS models. This work expands on previous research and formulates an efficient solution strategy to accommodate short-term variability and uncertainty into LTHS models, through gradually approximating the short-term dispatch problem per stage and under VRES uncertainty in the context of multi-stage Benders decomposition. The proposed solution strategy is embedded in an LTHS model based on stochastic dual dynamic programming and applied to a 2050 scenario of the Central and Northern European power system. We extract water values from the LTHS strategies and analyze their dependency on short-term variability.