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.
Related
- InFlow: entropic stochastic dual dynamic programming with HJB cross-certification for seasonal energy storage
- Stochastic Riccati equations with jumps for indefinite linear-quadratic control: A stochastic value-flow approach
- Continuous Policy and Value Iteration for Stochastic Control Problems with Inhomogeneous Synchronous Coupling
- Stochastic Galerkin Method for Fractional Boundary Value Problems: Convergence Analysis and Numerical Treatment
- Stochastic interpretation of the Water Waves equations
BibTeX
@misc{affognon2026stochastic,
title = {{Stochastic inflows and seasonal water value at Manantali}},
author = {Steeven B. Affognon and Babacar M. Ndiaye and Pierre Mendy and Cheikh M. F. Kebe and Sandrine Charousset and Omar Goudiaby and Thomas Ouillon},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.27744},
eprint = {2609.27744},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.27744},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Stochastic inflows and seasonal water value at Manantali
AU - Steeven B. Affognon
AU - Babacar M. Ndiaye
AU - Pierre Mendy
AU - Cheikh M. F. Kebe
AU - Sandrine Charousset
AU - Omar Goudiaby
AU - Thomas Ouillon
PY - 2026
DA - 2026/09/23
PB - arXiv
DO - 10.48550/arxiv.2609.27744
UR - https://arxiv.org/abs/2609.27744
AB - 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.
ER -