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Globally, the pressures of expanding populations, climate change, and increased energy demands are motivating significant investments in re-operationalizing existing reservoirs or designing operating policies for new ones. These challenges require an understanding of the tradeoffs that emerge across the complex suite of multi-sector demands in river basin systems. This study benchmarks our current capabilities to use Evolutionary Multi-Objective Direct Policy Search (EMODPS), a decision analytic framework in which reservoirs’ candidate operating policies are represented using parameterized global approximators (e.g., radial basis functions) then those parameterized functions are optimized using multi-objective evolutionary algorithms to discover the Pareto approximate operating policies. We contribute a comprehensive diagnostic assessment of modern MOEAs’ abilities to support EMODPS using the Conowingo reservoir in the Lower Susquehanna River Basin, Pennsylvania, USA. Our diagnostic results highlight that EMODPS can be very challenging for some modern MOEAs and that epsilon dominance, time-continuation, and auto-adaptive search are helpful for attaining high levels of performance. The ϵ-MOEA, the auto-adaptive Borg MOEA, and ϵ-NSGAII all yielded superior results for the six-objective Lower Susquehanna benchmarking test case. The top algorithms show low sensitivity to different MOEA parameterization choices and high algorithmic reliability in attaining consistent results for different random MOEA trials. Overall, EMODPS poses a promising method for discovering key reservoir management tradeoffs; however algorithmic choice remains a key concern for problems of increasing complexity. [ABSTRACT FROM AUTHOR]

Optimal management policies for water reservoir operation are generally designed via stochastic dynamic programming (SDP). Yet, the adoption of SDP in complex real-world problems is challenged by the three curses of dimensionality, modeling, and multiple objectives. These three curses considerably limit SDP's practical application. Alternatively, this study focuses on the use of evolutionary multiobjective direct policy search (EMODPS), a simulation-based optimization approach that combines direct policy search, nonlinear approximating networks, and multiobjective evolutionary algorithms to design Pareto-approximate closed-loop operating policies for multipurpose water reservoirs. This analysis explores the technical and practical implications of using EMODPS through a careful diagnostic assessment of the effectiveness and reliability of the overall EMODPS solution design as well as of the resulting Pareto-approximate operating policies. The EMODPS approach is evaluated using the multipurpose Hoa Binh water reservoir in Vietnam, where water operators are seeking to balance the conflicting objectives of maximizing hydropower production and minimizing flood risks. A key choice in the EMODPS approach is the selection of alternative formulations for flexibly representing reservoir operating policies. This study distinguishes between the relative performance of two widely-used nonlinear approximating networks, namely artificial neural networks (ANNs) and radial basis functions (RBFs). The results show that RBF solutions are more effective than ANN ones in designing Pareto approximate policies for the Hoa Binh reservoir. Given the approximate nature of EMODPS, the diagnostic benchmarking uses SDP to evaluate the overall quality of the attained Pareto-approximate results. Although the Hoa Binh test case's relative simplicity should maximize the potential value of SDP, the results demonstrate that EMODPS successfully dominates the solutions derived via SDP. [ABSTRACT FROM AUTHOR]

This paper introduces PySedSim, an open-source, object-oriented, Python-based, daily time step river basin simulation screening model for flow, sediment, and hydropower in networks of reservoirs and river channels. The model enables users to explore representations of four key concerns relevant to the selection and evaluation of alternative reservoir configurations: 1) management approaches to improve the passage of sediment through and around reservoirs to avoid storage capacity loss and downstream ecological impacts; 2) search for flexible and adaptive reservoir operating policies designed to achieve multiple objectives; 3) alternative design features such as dam gates, which are necessary to enable ecologically-focused reservoir features; and 4) uncertainties associated with hydroclimatic drivers and sediment processes. PySedSim is intended to support deliberative decision-making and design processes. We highlight PySedSim's functionality by demonstrating its use in a real decision context focused on identifying siting, design, and operation alternatives for the proposed Sambor mega Dam in Cambodia. Image 1 • Reservoir design and operation alternatives improve sediment passage. • Flexible problem formulation improves understanding of tradeoffs and alternatives. • Evaluating sediment, ecology, and hydropower together yields synergistic insights. • PySedSim supported iterative deliberation of alternatives to Sambor Dam (Cambodia). [ABSTRACT FROM AUTHOR]

Globally, many countries are actively seeking to maximize the hydropower potential of major river basins, yielding proposals for constructing approximately 3,700 major dams in the near future. At present, cost-benefit analyses are the dominant approach for evaluating candidate dam designs, where the typical problem framing has the following features: (i) a single objective, in which potentially conflicting and heterogeneous objectives are monetized and aggregated into a single Net Present Value (NPV) or Levelized Cost of Energy (LCE) metric; (ii) dam sizing using a pre-defined operating rule; and (iii) a limited accounting for potentially non-stationary external uncertainties. These features in standard cost-benefit analyses strongly bias dam design methods as they fail to explore the interdependent nature of reservoir sizing and operations. In particular, by neglecting a careful consideration of alternative reservoir operating schemes, standard design frameworks are likely to result in over-sized, under-performing dams. Moreover, deeply uncertain changes in hydro-climatic variability and human demands motivate the need for highly flexible assessment frameworks that capture the key tradeoffs between users, how dependencies in dam sizing and operations influence these tradeoffs, and the ultimate robustness of the infrastructure systems. In this work, we combine MORDM (Multi-Objective Robust Decision Making) and EMODPS (Evolutionary Multi-Objective Direct Policy Search) with the specific focus on advancing coupled dam sizing (i.e., discrete reservoir volumes) and operation design (i.e., continuous release policy parameters) problems. We demonstrate the potential of this integrated, highly flexible assessment framework through an ex post analysis of the Kariba dam in the Zambezi river basin. The Kariba dam was built in 1960 and has struggled to follow its specified operational rule curve that originally claimed to maximize hydropower production, independent of irrigation users' demands. Our results show that careful exploration of the coupling of dam sizing and operations yields designs that strongly outperforms the performance achieved under current Kariba system. Moreover, we demonstrate that our integrated sizing and operations framework leads to a significant reduction in capital cost requirements (i.e., smaller reservoir sizes) while simultaneously improving system robustness with respect to changing hydro-climatology and human irrigation demands. [ABSTRACT FROM AUTHOR]