Real‐options water supply planning: Multistage scenario trees for adaptive and flexible capacity expansion under probabilistic climate change uncertainty (2018)

Erfani T., K. Pachos, and J. Harou. (2018), Real‐options water supply planning: Multistage scenario trees for adaptive and flexible capacity expansion under probabilistic climate change uncertainty, Water Resour. Res.. https://doi.org/10.1029/2017WR021803

Planning water supply infrastructure includes identifying interventions that cost‐effectively secure an acceptably reliable water supply. Climate change is a source of uncertainty for water supply developments as its impact on source yields is uncertain. Adaptability to changing future conditions is increasingly viewed as a valuable design principle of strategic water planning. Because present decisions impact a system’s ability to adapt to future needs, flexibility in activating, delaying and replacing engineering projects should be considered in least‐cost water supply intervention scheduling. This is a principle of Real Option Analysis (ROA) which this paper applies to least‐cost capacity expansion scheduling via multistage stochastic mathematical programming. We apply the proposed model to a real‐world utility with many investment decision stages using a generalized scenario tree construction algorithm to efficiently approximate the probabilistic uncertainty. To evaluate the implementation of ROA, the use of two metrics is proposed: the Value of the Stochastic Solution (VSS) and the Expected Value of Perfect Information (EVPI) that quantify the value of adopting adaptive and flexible plans respectively. An application to London’s water system demonstrates the generalized approach. The investment decisions results are a mixture of long‐term and contingency schemes that are optimally chosen considering different futures. The VSS shows that by considering uncertainty, adaptive investment decisions avoid £100 million NPV cost, 15% of the total NPV. The EVPI demonstrates that optimal delay and early decisions have £50 million NPV, 6% of total NPV. Sensitivity of results to the characteristics of the scenario tree and uncertainty set is assessed.

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