An active learning approach for identifying the smallest subset of informative scenarios for robust planning under deep uncertainty

Giudici, Federico; Castelletti, Andrea; Giuliani, Matteo; Maier, Holger R.

doi:10.1016/j.envsoft.2020.104681

Cited by 29 publications

(11 citation statements)

References 56 publications

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“…Minimax, by identifying the best alternative in the worst possible input realization, encompasses the riskadverse behavior of the decision-maker. However, other criteria are available in the literature (see Giudici et al, 2020, for a review), with each of them representing a different level of risk perception and its associated definition of a robust operating policy. For a more comprehensive analysis, one could include additional robustness metrics in the experimental setup and evaluate how the choice of the metric shapes the selection of robust policy and the identification of the sources of vulnerability.…”

Section: Discussionmentioning

confidence: 99%

“…The main sources of uncertainty that we consider are the projected increase in water demand following urbanization (population uncertainty) and irrigation development (agricultural uncertainty) in the area, the magnitude of streamflow depletion due to climate change (climatic uncertainty), and the completion date of the Greater Maputo Water Supply Expansion Project (infrastructural uncertainty). The proposed methodology builds upon recent studies in the field of multiobjective reservoir operation (Giuliani et al, 2016b(Giuliani et al, , 2019Denaro et al, 2017;Zaniolo et al, 2018Zaniolo et al, , 2019 and of multiobjective robust decision-making (Giudici et al, 2020;Herman et al, 2015) by employing SA to investigate the role of uncertain exogenous drivers in shaping the effectiveness of optimal control policies across multiple competing sectors. So far, even though it has been recognized that optimal planning and control methods should employ SA to identify water resources system vulnerabilities to both structural and parametric uncertainties (Herman et al, 2019), only few studies have developed quantitative analyses to support water resource planning (e.g., Herman et al, 2015;Trindade et al, 2017Trindade et al, , 2019Groves et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Disentangling sources of future uncertainties for water management in sub-Saharan river basins

Amaranto

Juízo

Castelletti

2022

Hydrol. Earth Syst. Sci.

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Abstract. Water management in sub-Saharan African river basins is challenged by an uncertain future climatic, social and economical patterns potentially causing diverging water demands and availability, and by multi-stakeholder dynamics, resulting in evolving conflicts and tradeoffs. In such contexts, a better understanding of the sensitivity of water management to the different sources of uncertainty can support policymakers in identifying robust water supply policies balancing optimality and low vulnerability against likely adverse future conditions. This paper contributes an integrated decision-analytic framework combining an optimization, robustness, sensitivity, and uncertainty analysis to retrieve the main sources of vulnerability to optimal and robust reservoir operating policies across multi-dimensional objective spaces. We demonstrate our approach on the lower Umbeluzi river basin, Mozambique, which an archetypal example of sub-Saharan river basin, where surface water scarcity compounded by substantial climatic variability, uncontrolled urbanization rate, and agricultural expansion are hampering the Pequenos Libombos dam's ability to supply the agricultural, energy, and urban sectors. We adopt an Evolutionary Multi-Objective Direct Policy Search (EMODPS) optimization approach for designing optimal operating policies, whose robustness against social, agricultural, infrastructural, and climatic uncertainties is assessed via robustness analysis. We then implement the generalized likelihood uncertainty estimation (GLUE) and PAWN uncertainty and sensitivity analysis methods for disentangling the main challenges to the sustainability of the operating policies and quantifying their impacts on the urban, agricultural, and energy sectors. Numerical results highlight the importance of a robustness analysis when dealing with uncertain scenarios, with optimal non-robust reservoir operating policies largely being dominated by robust control strategies across all stakeholders. Furthermore, while robust policies are usually vulnerable only to hydrological perturbations and are able to sustain the majority of population growth and agricultural expansion scenarios, non-robust policies are sensitive also to social and agricultural changes and require structural interventions to ensure stable supply.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Disentangling sources of future uncertainties for water management in sub-Saharan river basins

Amaranto

Juízo

Castelletti

2022

Hydrol. Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…minimax, by identifying the best alternative in the worst possible input realization, encompasses a risk-adverse behavior of the decision maker. However, other criteria are available in the literature (see Giudici et al 2020 for a review), each of them representing a different level of risk perception and its associated definition of robust operating policy. For a more comprehensive analysis, one could include additional robustness metrics in the experimental setup, and evaluate how the choice of the metric shapes the selection of robust policy and the identification of the sources of vulnerability thereof.…”

Section: Discussionmentioning

confidence: 99%

Comment on hess-2021-40 - Disentangling Sources of Future Uncertainties forWater Management in Sub-Saharan River Basins by Amaranto et al.

Ciullo¹

2021

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Water management in sub-Saharan African river basins is challenged by uncertain future climatic, social and economical patterns, potentially causing diverging water demands and availability, as well as by multi-stakeholder dynamics, resulting in evolving conflicts and tradeoffs. In such contexts, a better understanding of the sensitivity of water management to the different sources of uncertainty can support policy makers in identifying robust water supply policies balancing optimality and low vulnerability against likely adverse future conditions. This paper contributes an integrated decision-analytic framework combining optimization, robustness, sensitivity and uncertainty analysis to retrieve the main sources of vulnerability to optimal and robust reservoir operating policies across multi-dimensional objective spaces. We demonstrate our approach onto the lower Umbeluzi river basin, Mozambique, an archetypal example of sub-Saharan river basin, where surface water scarcity compounded by substantial climatic variability, uncontrolled urbanization rate, and agricultural expansion are hampering the Pequenos Lipompos dam ability of supplying the agricultural, energy and urban sectors. We adopt an Evolutionary Multi-Objective Direct Policy Search optimization approach for designing optimal operating policies, whose robustness against social, agricultural, infrastructural and climatic uncertainties is assessed via robustness analysis. We then implement the GLUE and PAWN uncertainty and sensitivity analysis methods for disentangling the main challenges to the sustainability of the operating policies and quantifying their impacts on the urban, agricultural and energy sectors. Numerical results highlight the importance of robustness analysis when dealing with uncertain scenarios, with optimal-non robust reservoir operating policies largely dominated by robust control strategies across all stakeholders. Furthermore, while robust policies are usually vulnerable only to hydrological perturbations and are able to sustain the majority of population growth and agricultural expansion scenarios, non-robust policies are sensitive also to social and agricultural changes, and require structural interventions to ensure stable supply. IntroductionThe availability of freshwater is a limiting factor to food production, energy generation, and industrial consumption around the globe (Hermoso, 2017;Zampieri et al., 2018). Investing in new infrastructure is still the predominant option to expand storing and conveying capacity, particularly in sub-Saharan Africa and South-east Asia that have the larger untapped potential (Fields et al., 2009). Yet, this hard path water solution is evoking contentious debates for the considerable environmental and 1

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“…The electricity generation from hydroelectric power plants is greatly affected by the climate and environmental changes, due to which the supply uncertainties take place [13,14]. If these uncertainties in the supply are not taken care of, and the PSOs do not well plan the transmission and distribution systems, may result in socio-economic problems.…”

Section: B Related Workmentioning

confidence: 99%

Solver-Based Mixed Integer Linear Programming (MILP) Based Novel Approach for Hydroelectric Power Generation Optimization

et al. 2020

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In Pakistan, hydroelectric power is one of the reliable sources of electricity with a capacity of 8,713 MW, which is 29% of the total energy mix. Hence, with such a vast resource capacity of hydroelectric power, its optimization and dispatch planning will have a great significance. This research work discusses the importance of hydroelectric power generation planning for both storage and run-of-river (ROR) hydropower plants, as well as; a solver-based optimization technique is proposed for the first time to resolve the intricate job of generation planning for hydroelectric power plants in MATLAB. A mathematical-optimization model is also developed, which uses a Mixed-Integer Linear-Programming (MILP) algorithm, based on the objective function of profit maximization, which considers a random varying revenue plan as model input. Three hydropower generators of different capacities and efficiencies are considered for the optimization problem. MILP based solution is proposed for both storage and ROR hydropower plants with two dispatch schedules, i.e., Normal dispatch schedule and optimum dispatch schedule. The objective functions are solved, and the profit (in dollars) from each dispatch schedule is calculated and compared. The preliminary optimization results show an increase of $22,000 and $29,130 in the profit for storage and ROR hydropower plants, respectively, which is 19% more than average income. Hence, ensuring the credibility of the proposed algorithm for maximizing the revenue ($), is aimed to facilitate and assist better planning for electric power producers.

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An active learning approach for identifying the smallest subset of informative scenarios for robust planning under deep uncertainty

Cited by 29 publications

References 56 publications

Disentangling sources of future uncertainties for water management in sub-Saharan river basins

Disentangling sources of future uncertainties for water management in sub-Saharan river basins

Comment on hess-2021-40 - Disentangling Sources of Future Uncertainties forWater Management in Sub-Saharan River Basins by Amaranto et al.

Solver-Based Mixed Integer Linear Programming (MILP) Based Novel Approach for Hydroelectric Power Generation Optimization

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