Revised Min-Max (RMM) Approach for Two-Objective Reservoir Operation

Due to the changing climate, rapid development, and population growth, the current management of water resources is expected to be critically affected. The majority of reservoirs are multipurpose including water supply, flood control, hydropower production, etc., and often involve several competing interests. Most of the current reservoir management practices are ineffective, outdated, and highly subjective. Therefore, it is necessary to re-evaluate the current management rules for optimizing the objectives, reduction of water stress, and mitigation of climate change impacts.

Lake Como is a regulated lake in Northern Italy and the third largest lake, receiving water from the upper Adda River and controlled downstream by the “Olginate” regulation dam. The regulation dam has been constructed to manage the release according to irrigation and hydropower demand. In addition, it regulates the water level in the Lake within a certain threshold (i.e., upper, and lower bounds), to prevent flooding in the town of Como and to allow navigation and for environmental reasons.

This research mainly focuses on optimizing two conflicting objectives, the satisfaction of irrigation demand which is parametrized by α (the ratio between the actual release and the agricultural demand), and upstream flood regulation in the city of Como parametrized by β (the ratio between the actual active storage and the reference storage). Considering the characteristics of the reservoir and the targeted objectives, an optimal operating strategy has been developed by adopting a deterministic Revised Min-Max (RMM) approach. It focuses on the determination of the minimum water level required to satisfy the irrigation demand and the maximum water level to avoid flooding for a specified value of α and β. This approach is based on simulating the continuity equation for a set of 71 years of inflow and outflow time series, from 1946 (the operation of the Olginate dam began) to 2016. besides, the outflow time series was used to simulate the current management policy and historical efficiency of the system in terms of α and β.

Out of the several feasible solutions (combinations of α and β), we are interested in efficient (Pareto optimal) solutions, where there are no other solutions that can improve either α and/or β. We evaluate three possible management strategies depending on the storage condition and the feasible solutions with different combinations of α and β through a trade-off analysis. The first tends to approach historical average levels (BAUL: Business As Usual Level); the second approaches the historical average releases (BAUR: Business As Usual Release); the third one allows to modulate of the releases with the parameter delta (0< δ <1), which tends to satisfy irrigation demand (δ=0) or flood control (δ=1). In summary, this study shows that the current operating rule can be substantially improved with respect to both objectives, with an improvement of 19% in terms of irrigation demand satisfaction and 69% in terms of flood control.