Many‐objective reservoir policy identification and refinement to reduce policy inertia and myopia in water management

Giuliani, Matteo; Herman, Jonathan D.; Castelletti, Andrea; Reed, Patrick M.

doi:10.1002/2013wr014700

Cited by 143 publications

(96 citation statements)

References 90 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: 88%

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: 88%

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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“…The decision analytic framework proposed by Giuliani et al (2014) combines reservoir policy identification and many-objective optimization under uncertainty to characterize the current baseline operations of the Conowingo reservoir and discover key tradeo↵s between alternative operating policies. In Giuliani et al (2014), two formulations of the MODPS problem are contrasted: the historical case, as formulated in eq.…”

Section: Application Resultsmentioning

confidence: 99%

“…They are defined as multiinput multi-output RBFs (accounting for 32 parameters), which provide the release decision vector u t (i.e., the downstream release as well as the ones for the public water supply) as a function of time t and reservoir level h t . Six operating objectives measures the multi-stakeholder interests a↵ected by the Conowingo reservoir operation (for further details about the problem formulation see Giuliani et al, 2014): -Hydropower revenue (to be maximized); -Atomic Power Plant, Baltimore, and Chester water supply volumetric reliability (to be maximized); -Recreation, modeled by the storage reliability (to be maximized); -Environment, modeled by the shortage index with respect to FERC flow requirements (to be minimized).…”

Section: Case Study Descriptionmentioning

confidence: 99%

“…The method is demonstrated on the management problem of the Conowingo Dam, located within the Lower Susquehanna River, USA. The Lower Susquehanna River is an interstate water body that has been subject to intensive water management e↵orts due to competing demands from urban water supply, atomic power plant cooling, hydropower production, federally regulated environmental flows, and recreational interests (Giuliani et al, 2014). We first compute the Pareto-optimal control policies explicitly considering all the water uses involved.…”

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confidence: 99%

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Improving the protection of aquatic ecosystems by dynamically constraining reservoir operation via direct policy conditioning

Giuliani

Castelletti

Reed

2014

IFAC Proceedings Volumes

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Abstract:Water management problems generally involve conflicting and non-commensurable objectives. Assuming a centralized perspective at the system-level, the set of Pareto-optimal alternatives represents the ideal solution of most of the problems. Yet, in typical real-world applications, only a few primary objectives are explicitly considered, taking precedence over all other concerns. These remaining concerns are then internalized as static constraints within the problem's formulation. This approach yields to solutions that fail to explore the full set of objectives tradeo↵s. In this paper, we propose a novel method, called direct policy conditioning (DPC), that combines direct policy search, multi-objective evolutionary algorithms, and input variable selection to design dynamic constraints that change according to the current system conditions. The method is demonstrated for the management problem of the Conowingo Dam, located within the Lower Susquehanna River, USA. The DPC method is used to identify environmental protection mechanisms and is contrasted with traditional static constraints defining minimum environmental flow requirements. Results show that the DPC method identifies a set of dynamically constrained control policies that overcome the current alternatives based on the minimum environmental flow constraint, in terms of environmental protection but also of the primary objectives.

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“…Regarding the decision variables used in optimisation-based WRM approaches, these are almost exclusively modelled as deterministic. However, this often results in rigid, precautionary strategies that may not be sufficiently flexible to adapt to uncertain future changes (Basupi and Kapelan;Giuliani et al, 2014b;Mortazavi et al, 2013a;Woodward et al, 2014).…”

Section: Current Statusmentioning

confidence: 99%

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Maier

Kapelan

Kasprzyk

et al. 2014

Environmental Modelling & Software

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523

317

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In all cases accepted manuscripts should:  link to the formal publication via its DOI  bear a CC-BY-NC-ND license -this is easy to do, click here to find out how  if aggregated with other manuscripts, for example in a repository or other site, be shared in alignment with our hosting policy  not be added to or enhanced in any way to appear more like, or to substitute for, the published journal article AbstractThe development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g., model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts.

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Many‐objective reservoir policy identification and refinement to reduce policy inertia and myopia in water management

Cited by 143 publications

References 90 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

Improving the protection of aquatic ecosystems by dynamically constraining reservoir operation via direct policy conditioning

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

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