Adaptive, multiobjective optimal sequencing approach for urban water supply augmentation under deep uncertainty

Beh, Eva H. Y.; Maier, Holger R.; Dandy, Graeme C.

doi:10.1002/2014wr016254

Cited by 100 publications

(73 citation statements)

References 61 publications

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“…Examples of the application of RO in the development of long-term water management strategies can be found in Kwakkel et al (2014), Giuliani et al (2014), Herman et al (2014), Kang and Lansey (2013) and Beh et al (2015b) and for adaptive policymaking in Hamarat et al (2012). Within this research it was found that RO could handle complex, deeply uncertain problems with large numbers of possible solutions.…”

Section: The Current Approach Within the Uk As Stated In The Environmentioning

confidence: 87%

“…percentage) of future supply and demand scenarios that result in an acceptable system performance (Paton et al 2014a;Beh et al 2015b), i.e. as follows:…”

Section: Robustness Of Water Supplymentioning

confidence: 99%

“…Although this approach is also not without its drawbacks, i.e. when deliberating on final trade-offs, as discussed by Beh et al (2015b); however methods exist to aid the final decision process, such as value path plots (Geoffrion et al 1972) and visual analytics (Reed and Kollat 2013 (Borgwardt 1987) or combined process approaches such as Many-Objective Visual Analytics (Fu et al 2013), Many-Objective Robust Decision Making (MORDM) or Borg MultiObjective Evolutionary Algorithms (MOEA) (Hadka and Reed 2012).…”

Section: Robust Optimisation (Ro)mentioning

confidence: 99%

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Comparison of Robust Optimization and Info-Gap Methods for Water Resource Management under Deep Uncertainty

Roach

Kapelan

Ledbetter

et al. 2016

J. Water Resour. Plann. Manage.

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This paper evaluates two established decision making methods and analyses their performance and suitability within a Water Resources Management (WRM) problem. The methods under assessment are Info-Gap decision theory (IG) and Robust Optimisation (RO). The methods have been selected primarily to investigate a contrasting local vs global method of assessing water system robustness to deep uncertainty but also to compare a robustness model approach (IG) with a robustness algorithm approach (RO), whereby the former selects and analyses a set of prespecified strategies and the latter uses optimisation algorithms to automatically generate and evaluate solutions. The study presents a novel area-based method for IG robustness modelling and assesses the applicability of utilising the Future Flows climate change projections in scenario generation for water resource adaptation planning. The methods were applied to a case study 2 resembling the Sussex North Water Resource Zone in England, assessing their applicability at improving a risk-based WRM problem and highlighting the strengths and weaknesses of each method at selecting suitable adaptation strategies under climate change and future demand uncertainties. Pareto sets of robustness to cost are produced for both methods and highlight RO as producing the lower costing strategies for the full range of varying target robustness levels. IG produced the more expensive Pareto strategies due to its more selective and stringent robustness analysis, resulting from the more complex scenario ordering process.

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Section: The Current Approach Within the Uk As Stated In The Environmentioning

confidence: 87%

“…percentage) of future supply and demand scenarios that result in an acceptable system performance (Paton et al 2014a;Beh et al 2015b), i.e. as follows:…”

Section: Robustness Of Water Supplymentioning

confidence: 99%

Section: Robust Optimisation (Ro)mentioning

confidence: 99%

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Comparison of Robust Optimization and Info-Gap Methods for Water Resource Management under Deep Uncertainty

Roach

Kapelan

Ledbetter

et al. 2016

J. Water Resour. Plann. Manage.

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“…Paton et al (2014) developed a framework for water supply system planning with alternative sources and climate change considerations, while Beh et al (2014Beh et al ( , 2015 developed two methods for optimal sequencing of urban water supply augmentation options under deep uncertainty regarding demands and climate. The research by both Paton et al (2014) and Beh et al (2014Beh et al ( , 2015 considered only the planning of water supply projects, and did not optimize the specific design or operation of the systems. Sequencing is also considered in Cai et al (2015), however, in this case it is applied to planning of drought mitigation strategies in agricultural systems.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Within the current climate, there may be multiple realizations of possible rainfall and streamflow series (for example dry or wet years). Beh et al (2015) considered rainfall, as well as population and temperature, as uncertain variables in their optimal sequencing methodology for water supply system augmentation. They considered both climate and hydrologic variability: seven possible future climate scenarios provided different forecasted rainfall reductions, and within each of these seven scenarios, 20 stochastic replicates of the rainfall sequence were produced.…”

Section: Rainfall and Streamflowmentioning

confidence: 99%

Framework for the optimization of operation and design of systems with different alternative water sources

et al. 2017

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Water security has become an increasing concern for many water system managers due to climate change and increased population. In order to improve the security of supply, alternative sources such as harvested stormwater, recycled wastewater and desalination are becoming more commonly used. This brings about the need for tools to analyze and optimize systems that use such sources, which are generally more complex than traditional water systems. Previous methodologies have been limited in their scope and cannot be applied to all types of water sources and systems. The framework presented in this paper has been developed for holistic analysis and optimization of water supply and distribution systems that use alternative water sources. It includes both design and operational decision variables, water and energy infrastructure, simulation of systems, analysis of constraints and objectives, as well as policies and regulations which may affect any of these factors. This framework will allow users to develop a comprehensive analysis and/or optimization of their water supply system, taking into account multiple types of water sources and consumers, the effect of their own design and operational decisions, and the impact of government policies and different energy supply options. Two case study systems illustrate the application of the framework; the first case study is a harvested stormwater system that is used to demonstrate the importance of simulation and analysis prior to optimization, the second utilizes four different water sources to increase security of supply and was optimized to reduce pump energy use.

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Run‐of‐river power plants in Alpine regions: Whither optimal capacity?

Lazzaro

Botter

2015

Water Resources Research

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Although run-of-river hydropower represents a key source of renewable energy, it cannot prevent stresses on river ecosystems and human well-being. This is especially true in Alpine regions, where the outflow of a plant is placed several kilometers downstream of the intake, inducing the depletion of river reaches of considerable length. Here multiobjective optimization is used in the design of the capacity of run-of-river plants to identify optimal trade-offs between two contrasting objectives: the maximization of the profitability and the minimization of the hydrologic disturbance between the intake and the outflow. The latter is evaluated considering different flow metrics: mean discharge, temporal autocorrelation, and streamflow variability. Efficient and Pareto-optimal plant sizes are devised for two representative case studies belonging to the Piave river (Italy). Our results show that the optimal design capacity is strongly affected by the flow regime at the plant intake. In persistent regimes with a reduced flow variability, the optimal trade-off between economic exploitation and hydrologic disturbance is obtained for a narrow range of capacities sensibly smaller than the economic optimum. In erratic regimes featured by an enhanced flow variability, instead, the Pareto front is discontinuous and multiple trade-offs can be identified, which imply either smaller or larger plants compared to the economic optimum. In particular, large capacities reduce the impact of the plant on the streamflow variability at seasonal and interannual time scale. Multiobjective analysis could provide a clue for the development of policy actions based on the evaluation of the environmental footprint of run-of-river plants.

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Adaptive, multiobjective optimal sequencing approach for urban water supply augmentation under deep uncertainty

Cited by 100 publications

References 61 publications

Comparison of Robust Optimization and Info-Gap Methods for Water Resource Management under Deep Uncertainty

Comparison of Robust Optimization and Info-Gap Methods for Water Resource Management under Deep Uncertainty

Framework for the optimization of operation and design of systems with different alternative water sources

Run‐of‐river power plants in Alpine regions: Whither optimal capacity?

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