This article presents an innovative streamflow process model for use in reservoir operational rule design in stochastic dual dynamic programming (SDDP). Model features, which can be applied independently, are (1) a multiplicative process model for the forward phase and its linearized version for the backward phase; and (2) a nonuniform time-step length that is inversely proportional to seasonal variability. The advantages are (1) guaranteeing positive streamflow values still maintaining problem linearity, and (2) making system adaptation faster during high variable periods. Model identification is straightforward, as with the additive periodic autoregressive model generally used in SDDP. The proposed model is applied on the Senegal River system for the optimal operation of Manantali Reservoir and evaluated against the streamflow process model currently used in the water management literature.

Model predictive control (MPC) can be employed for optimal operation of adjustable hydraulic structures. MPC selects the control to be applied to the system by solving an optimization problem over a finite horizon in real-time. The horizon finiteness is both the reason for MPC’s success and its main limitation. MPC has in fact been successfully employed for short-term reservoir management. Short-term reservoir management deals effectively with fast processes, such as flood, but it is not capable of looking sufficiently ahead to handle long-term issues, such as drought. This study proposes an infinite horizon MPC solution that deals with both short and long-term objectives, tailored for reservoir management. In the proposed solution, the control signal is structured by the use of basis functions. Basis functions reduce the optimization argument to a small number of variables, making the control problem solvable in a reasonable time. The solution is tested for the operational management of Manantali...

The basin of the Seine River is an extremely important economic region for France and Europe. Four reservoirs are operated to reduce the natural variability of the Seine River, reducing both flood and drought risk. Presently, reservoir operation is not centrally coordinated, and release rules are based on empirical rule curves. This study presents the setting of an optimal and centralized solution to the problem of reservoir operation on the Upper Seine–Aube river system, found by applying the stochastic dual dynamic programming (SDDP) procedure. The novelty of this study lies on the combination of reservoir and hydraulic models in SDDP for flood and drought protection. Including the hydraulic process in SDDP is required for estimating flood and drought at different locations along the river, and for representing the delay between the release from the reservoirs and their effects downstream. The study case covers the Seine basin until the confluence with the Aube River: this system includes two reservoirs, the city of Troyes, France, and, at the confluence of the two rivers, the nuclear power plant at Nogent-Sur-Seine. Results shows that the SDDP solution can be effectively used to optimize the operation of a water system made of multiple reservoirs and multiple hydraulic transfer components, solving a relatively large stochastic dynamic programming problem in an acceptable time. The management obtained from SDDP rules exploits the centralized operation and, compared to the current operational rules, results in more frequent but shorter, less intense, and less severe flood and drought events at Nogent-Sur-Seine.