Improving the Robustness of Reservoir Operations with Stochastic Dynamic Programming

Kim, Gi Joo; Kim, Young-Oh; Reed, Patrick M.

doi:10.1061/(asce)wr.1943-5452.0001381

Cited by 17 publications

(5 citation statements)

References 86 publications

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“…SDP algorithms are still widely applied and have mild requirements to system representation (Giuliani et al 2021), allowing representation nonconvexities. Variants of the SDP model, such as sampling stochastic dynamic programming (SSDP) (Kelman et al 1990;Côté and Arsenault 2019) and robust stochastic dynamic programming (RSDP) (Kim et al 2021) has extended the applicability of this type of algorithm. However, because state variables need to be discretized in the SDP algorithm, it suffers from the "curse of dimensionality," leading to computationally intractable problems when considering systems with a large number of state variables.…”

Section: Introductionmentioning

confidence: 99%

Hydropower Scheduling with State-Dependent Discharge Constraints: An SDDP Approach

Helseth

Hågenvik

et al. 2022

J. Water Resour. Plann. Manage.

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Environmental constraints in hydropower systems serve to ensure sustainable use of water resources. Through accurate treatment in hydropower scheduling, one seeks to respect such constraints in the planning phase while optimizing the utilization of hydropower. However, many environmental constraints introduce state-dependencies and even nonconvexities to the scheduling problem, making them challenging to represent in stochastic hydropower scheduling models. This paper describes how the state-dependent maximum discharge constraint, which is widely enforced in the Norwegian hydropower system, can be embedded within the stochastic dual dynamic programming (SDDP) algorithm for hydropower scheduling without compromising computational time. In this work, a combination of constraint relaxation and time-dependent auxiliary lower reservoir volume bounds is applied, and the modeling is verified through computational experiments on two different systems. The results demonstrate that the addition of an auxiliary lower bound on reservoir volume has significant potential for improved system operation, and that a bound based on the minimum accumulated inflow in the constraint period is the most efficient.

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Section: Introductionmentioning

confidence: 99%

Hydropower Scheduling with State-Dependent Discharge Constraints: An SDDP Approach

Helseth

Hågenvik

et al. 2022

J. Water Resour. Plann. Manage.

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“…Robust and flexible planning can mitigate the impacts of climate uncertainty on water systems. Robust planning aims to design infrastructure systems that achieve adequate performance across a wide range of uncertain, plausible future scenarios (Groves & Lempert, 2007; Herman et al., 2015; Kim et al., 2021; Lempert et al., 2006). However, this approach can favor the construction of large, static infrastructure projects that leverage economies of scale but are difficult to adapt to future conditions.…”

Section: Introductionmentioning

confidence: 99%

Valuing Combinations of Flexible Planning, Design, and Operations in Water Supply Infrastructure

Willebrand,

Zaniolo,

Skerker

et al. 2024

Water Resources Research

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Uncertainty arising from climate change poses a central challenge to the long‐term performance of many engineered water systems. Water supply infrastructure projects can leverage different types of flexibility, in planning, design, or operations, to adapt infrastructure systems in response to climate change over time. Both flexible planning and design enable future capacity expansion if‐and‐when needed, with flexible design proactively incorporating physical design changes that enable retrofits. All three forms of flexibility have not previously been analyzed together to explicitly assess their relative value in mitigating cost and water supply reliability risk. In this paper, we propose a new framework to evaluate combinations of flexible planning, design, and operations. We develop a nested stochastic dynamic optimization approach that jointly optimizes dam development and operating policies under dynamic climate uncertainty. We demonstrate this approach on a reservoir project near Mombasa, Kenya. Our results find that flexible operations have the greatest potential to reduce costs. Flexible design and flexible planning can amplify the value of flexible operations under higher discounting scenarios and when initial infrastructure capacities are undersized. This approach provides insight on the climate change and techno‐economic conditions under which flexible planning, design, and operations can be best leveraged individually or in combination to reduce climate change uncertainty risks in water supply infrastructure projects.

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“…Recently, [81] have provided a brief overview of different techniques and compared six different state-of-the-art modelling techniques, including Deterministic Dynamic Programming (DDP), Stochastic Dynamic Programming (SDP), Implicit Stochastic Optimization (ISO), Fitted Q-Iteration (FQI), Sampling Stochastic Dynamic Programming (SSDP), and Model Predictive Control (MPC). Dynamic Programming (DP) has been a successful optimization technique for reservoir operations, which provides a systematic procedure that decomposes a multistage decisionmaking problem into a series of single-stage decision-making problems using the principle of optimality [82,83,84]. Similarly, SDP is a popular technique to handle stochasticity of inflow to the reservoir by taking into account uncertainty associated with forecasts [85].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Reservoir Operation With Demand Uncertainty and Internal Climate Variability

Upadhyay,

Bhatia

2024

Preprint

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Climate change projections, essential for water resource management, are dominated by multiple sources of uncertainty, including scenario uncertainty and Internal Climate Variability (ICV). The latter is often classified as irreducible and thereby overlooked in traditional decision-making processes on regional and local scales. Our study translates these traditionally irreducible uncertainties into actionable insights by leveraging multiple initial condition ensemble (MICE) outputs, a physically based hydrological model and a multiobjective stochastic optimisation approach. We focus on the Sardar Sarovar Dam in Gujarat, India, a multipurpose reservoir integral to flood control, hydropower generation and meeting diverse water demands under two future climate scenarios (SSP245 and SSP585). We find that for the multipurpose reservoir under consideration, despite considering a broad spectrum of outputs, which are equally plausible due to the inherent variability of the climate, the system is highly reliable for meeting the drinking water supply for the region for the next century, although at the cost of agricultural and industrial water supply. Incorporating ICV provides a robust assessment of system attributes, including reliability, vulnerability, and resilience, particularly when these objectives are in trade-off with each other. Our study emphasizes the critical role of accounting for ICV in water resource planning. This analytical approach allows stakeholders to pinpoint specific vulnerabilities, allowing targeted planning for more adaptable and resilient water resource management strategies, including sustainable water supply and flood control.

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Improving the Robustness of Reservoir Operations with Stochastic Dynamic Programming

Cited by 17 publications

References 86 publications

Hydropower Scheduling with State-Dependent Discharge Constraints: An SDDP Approach

Hydropower Scheduling with State-Dependent Discharge Constraints: An SDDP Approach

Valuing Combinations of Flexible Planning, Design, and Operations in Water Supply Infrastructure

Optimizing Reservoir Operation With Demand Uncertainty and Internal Climate Variability

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