Abstract
AbstractPlanning 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 Options Analysis, 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 Real Options Analysis, the use of two metrics is proposed: the value of the stochastic solution and the expected value of perfect information 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 value of the stochastic solution shows that by considering uncertainty, adaptive investment decisions avoid £100 million net present value (NPV) cost, 15% of the total NPV. The expected value of perfect information 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.
Highlights
Introduction and BackgroundWater utilities aim to maintain an efficient and reliable water supply service by optimally combining the scheduling of supply augmentation projects and demand reduction policies (Mortazavi-Naeini et al, 2014)
This is a principle of Real Options Analysis, which this paper applies to least-cost capacity expansion scheduling via multistage stochastic mathematical programming
Solving the Water Resource Planning Problem at Multiple Stages Over Time Figure 4 shows the nine supply scenarios in planning decision period 2020–2024, at t2 magnified from the scenario tree
Summary
Water utilities aim to maintain an efficient and reliable water supply service by optimally combining the scheduling of supply augmentation projects and demand reduction policies (Mortazavi-Naeini et al, 2014). Deterministic supply-demand optimization aims to meet service levels commitments under historically dire conditions and identifies a fixed least-cost schedule of system upgrades (Padula et al, 2013). Fixed investment plans are brittle; that is, if future conditions turn out to be different than assumed, the plan is likely to fail (Chung et al, 2009). The antidote to brittleness is robustness (defined as a decision that performs acceptably well over a range of conditions) and flexibility (defined as the ability to switch a decision depending on outcomes that materialize; Maier et al, 2016). Methods that use an ensemble of plausible scenarios to seek robustness and flexibility are discussed below
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