Stochastic inflows and seasonal water value at Manantali

Abstract

Seasonal hydropower operation requires release decisions that account for uncertain future inflows and the opportunity cost of stored water. We assess the Modelling and Optimisation of Stochastic Seasonal Hydropower Storage (MOSSHOOS) framework for the Manantali reservoir using 660 monthly energy-inflow observations from 1961--2015. The framework combines multiscale inflow representation, blocked resampling and scenario reduction with stochastic dual dynamic programming, and uses a seasonal square-root diffusion and Hamilton--Jacobi--Bellman formulation as a continuous-time interpretation of marginal water value. Scenario reduction improves the energy score by 47.6\% relative to independent monthly sampling, but the marginal distribution is rejected by a Kolmogorov--Smirnov test and the lower-tail p10 error remains 103.9\%, indicating inadequate dry-tail calibration. Consistent with that limitation, 50 paired Monte Carlo evaluations show no detectable cost or deficit advantage of the stochastic policy over its deterministic-equivalent counterpart at the archived operating configuration. As an independent benchmark, the repaired open-source \texttt{plan4res} seasonal storage valuation chain yields a dominant marginal value of 81.66~USD/MWh, within 3\% of the MOSSHOOS mean of 79.25~USD/MWh. These results support a coherent stochastic inflow-to-water-value methodology while showing that hydrological tail representation, rather than economic scaling, is the principal limitation to address before operational application.