The El Niño Southern Oscillation (ENSO) is a major driver of global hydro‐climatic variability, with well‐known effects on floods, droughts, and coupled human‐natural systems. Its impact on urban settlements depends on both level of exposure and preparedness; two factors that are responsible for severe cuts on millions of people in developing countries, where urban water supply relies almost entirely on rainfall‐dependent sources. To understand whether information on the ENSO state could help mitigate the effects of droughts, we use Metro Manila's water supply system as exemplifying case study, for which we design “traditional” and adaptive management policies. The former are based on information typically available to operators, such as reservoir storage; the latter complement such information with the Oceanic Niño Index (ONI)—an indicator used for monitoring El Niño and La Niña state. Results obtained by comparing the policy performance on a large set of stochastic streamflow and ONI replicates show that ENSO‐informed policies are more robust, meaning that they attain a minimum performance level across a broader set of replicates. We show that the primary cause of this behavior is the information on the ENSO state. To further quantify the value of the ONI, we then compare the performance of a representative ENSO‐informed policy and the system's current operating rules on the period 1968–2014. The comparison shows that the severe water supply restrictions caused by the existing management system could have been partially avoided through a sequence of smaller restrictions implemented at the onset of the main El Niño events.
<p>Hydro power assets contribute a valuable share of carbon-free energy generation worldwide. Large reservoirs are able to store energy and, combined with pump-storage capacities, they will play an important role in the future&#8217;s energy mix. In the future, the stronger integration of volatile energy sources, like solar and wind energy demands the flexibility of hydro power plants. In general, the operation of hydro power plants is a multi-stakeholder and multi-objective dynamic problem related to critical infrastructure. This requires flexible and robust reservoir operation policies, defined as closed-loop release functions where the system state is the input and turbine flows are the response of the function. Recently, Evolutionary-Multi-Objective-Direct-Policy-Search (EMODPS) yielded promising control policies for water resources systems. EMODPS is a kind of machine learning approach that relies on long records, or stochastic streamflow replicates capturing a wide range of possible conditions. A stochastic streamflow generator should actually cover all possible conditions related to the state-action-space and inflates the optimization process. Furthermore, the search procedure can implicitly identify the "most representative" states of the system and tends to approximate a better solution for these states. States that are very rarely explored but can be very important for a reliable operation have little effect on the optimized policy. In addition, artificial neuronal networks (ANN) derived from EMODPS suffer under the curse of instable sections . This is because ANN's are good at interpolating, but bad at extrapolating actions from unobserved states in the training sequence. Thus, we extend the well-known EMODPS framework by an re-optimizing approach utilizing seasonal streamflow predictions. Periodically, the reservoir policies are re-optimized based on an ensemble of streamflow predictions and the actual reservoir water levels. This adaptive policy search (APS) approach is applied to a three reservoirs cascade under Mediterranean climate, where the energy market will play an important role in the future. First results show that the hydropower operation can be improved: energy generation can slightly be increased at clearly lower cost of flood risk compared to static robust policies.</p>
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