Implications of Preference and Problem Formulation on the Operating Policies of Complex Multi-Reservoir Systems

Optimizing the operations of multi-reservoir systems poses several challenges: 1) the high dimension of the problem’s states and controls, 2) the need to balance conflicting multi-sector objectives, and 3) understanding how uncertainties impact system performance. These difficulties motivated the development of the Evolutionary Multi-Objective Direct Policy Search (EMODPS) framework, in which multi-reservoir operating policies are parameterized in a given family of functions and then optimized for multiple objectives through simulation over a set of stochastic inputs. However, properly framing these objectives remains a severe challenge and a neglected source of uncertainty. Here, we use EMODPS to optimize operating policies for a 4-reservoir system in the Red River Basin in Vietnam, exploring the consequences of optimizing to different sets of objectives related to 1) hydropower production, 2) meeting multi-sector water demands, and 3) providing flood protection to the capital city of Hanoi. We show how coordinated operation of the reservoirs can differ markedly depending on how decision makers weigh these concerns. Moreover, we illustrate how formulation choices that emphasize the mean, tail, or variability of performance across objective combinations must be evaluated carefully. Our results show that these choices can significantly improve attainable system performance, or yield severe unintended consequences. Finally, we show that satisfactory validation of the operating policies on a set of out-of-sample stochastic inputs depends as much or more on the formulation of the objectives as on effective optimization of the policies. These observations highlight the importance of carefully considering how we abstract stakeholders’ objectives and of iteratively optimizing and visualizing multiple problem formulation hypotheses to ensure that we capture the most important tradeoffs that emerge from different stakeholder preferences.