Choosing small sets of policy-relevant scenarios by combining vulnerability and diversity approaches

Carlsen, Henrik; Lempert, Robert J.; Wikman–Svahn, Per; Schweizer, Vanessa

doi:10.1016/j.envsoft.2016.06.011

Cited by 37 publications

(29 citation statements)

References 37 publications

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“…When choosing which scenarios should be included in this small set, caution is necessary to guarantee that the selected scenarios represent sufficiently diverse futures and exhaust key uncertainties associated with the energy transition [18,47]. There are existing methods for characterizing and filtering a large number of energy scenarios to derive a small and diverse set of…”

Section: Selection Of Maximally-different Scenarios To Assure Diversitymentioning

confidence: 99%

“…It locates a scenario set with maximal sum of mean weighted Manhattan distances of all consistent scenarios, when the minimal weighted Manhattan distance is exogenously given as a threshold. [18,49] Local Efficiency This method is tailored to qualitative and semi-quantitative scenario construction. In the large scenario set, consistency score of every scenario is evaluated.…”

Section: Selection Of Maximally-different Scenarios To Assure Diversitymentioning

confidence: 99%

“…Their approach, however, has resulted in very large uncertainty bounds, which could be meaningfully reduced by employing energy systems models to exclude internally inconsistent scenarios. Recent advances in semi-quantitative scenario techniques include selection of a small set of diverse scenarios in order to guarantee the variety of futures considered [18,19]. However, such diversity techniques have been demonstrated for semi-quantitative scenarios only.…”

Section: Introductionmentioning

confidence: 99%

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Ensuring diversity of national energy scenarios: Bottom-up energy system model with Modeling to Generate Alternatives

Berntsen

Trutnevyte

2017

Energy

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Long-range energy scenarios are commonly used to inform national energy policy decisions. Although the scenario approach aims to expand the spectrum of futures considered, in the past energy scenarios have not been diverse enough to include less expected real-world developments. We use a bottom-up energy system model EXPANSE with Modeling to Generate Alternatives (MGA) to assess the diversity of the existing ensemble of multi-organization, multi-model Swiss electricity supply scenarios. We show that both for 2035 and 2050 existing scenarios cover well the various possibilities in deployment of individual electricity generation technologies in terms of installed capacity or produced electricity. When analyzing scenarios as a whole, we find that in 2035 there is a lack of scenarios with high electricity demand and greater deployment of renewable technologies occurring simultaneously, as well as scenarios with a negligible or negative share of net electricity import. In 2050, there is also a lack of scenarios that depict high electricity demand and conservative deployment of new renewable technologies, especially solar PV. We propose six additional scenarios to be included to increase the diversity of the ensemble of Swiss electricity supply scenarios and achieve better understanding of the future possibilities and uncertainties.

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Section: Selection Of Maximally-different Scenarios To Assure Diversitymentioning

confidence: 99%

Section: Selection Of Maximally-different Scenarios To Assure Diversitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ensuring diversity of national energy scenarios: Bottom-up energy system model with Modeling to Generate Alternatives

Berntsen

Trutnevyte

2017

Energy

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“…We provided a proof of principle implementation by adapting ε-NSGAII (Kollat & Reed, 2006). The MOEA approach enables easy extensions by bringing in additional scenario relevant considerations such as diversity and consistency (Carlsen, Lempert, Wikman-Svahn, & Schweizer, 2016;Eker & Kwakkel, 2018). Within an MOEA framework, these can easily be added as additional objectives, rather than be calculated in postprocessing as with PRIM.…”

Section: Con Clus Ionmentioning

confidence: 99%

A generalized many‐objective optimization approach for scenario discovery

Kwakkel

2019

Futures & Foresight Science

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Scenario discovery is a model‐based approach for scenario development, aimed at finding one or more subspaces within the uncertainty space associated with a model that is decision‐relevant. These identified subspaces can subsequently be translated into narratives or shared in other ways a broader participatory process. Finding such as subspace involves solving a three‐objective optimization problem. A subspace should cover many of the decision relevant model runs, while containing as few as possible nondecision relevant model runs, and being easy to interpret. Existing techniques for scenario discovery, however, focus only on finding a subspace that minimizes the number of nondecision relevant model runs. Adopting a single objective optimization approach for a many‐objective optimization problem implies that the full trade‐off space is not identified. In this paper, we introduce a many‐objective optimization approach for scenario discovery. We compare this with an improved usage of Patient Rule Induction Method (PRIM) for identifying the multidimensional trade‐offs amongst coverage, density, and interpretability. We find that the many‐objective optimization approach produces results which slightly dominate those of the improved version of PRIM on all three objectives. Qualitatively, however, both approaches identify essentially the same subspaces. The prime benefits of the many‐objective optimization approach are its potential in bringing additional scenario relevant concerns such as consistency or diversity into the scenario discovery framework, as well as its ability to avoid overfitting. Potentially more important, it also paves the way for future work on using more sophisticated metaheuristic optimization approaches for scenario discovery.

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“…Climate selection followed the methods of Carlsen et al (2016). Briefly, an objective function accounting for variance in input models, climate model outcome space, and the range of model outcomes was maximized over 100,000 random samples.…”

Section: Approach To Selecting Climate Projectionsmentioning

confidence: 99%

Evaluation of the Jinan City Water Ecological Development Implementation Plan and Recommendations for Improvement

Groves¹,

Knopman²,

Syme³

et al. 2017

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Choosing small sets of policy-relevant scenarios by combining vulnerability and diversity approaches

Cited by 37 publications

References 37 publications

Ensuring diversity of national energy scenarios: Bottom-up energy system model with Modeling to Generate Alternatives

Ensuring diversity of national energy scenarios: Bottom-up energy system model with Modeling to Generate Alternatives

A generalized many‐objective optimization approach for scenario discovery

Evaluation of the Jinan City Water Ecological Development Implementation Plan and Recommendations for Improvement

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