Characterization of input uncertainties in strategic energy planning models

Moret, Stefano; Gironès, Víctor Codina; Bierlaire, Michel

doi:10.1016/j.apenergy.2017.05.106

Cited by 111 publications

(109 citation statements)

References 42 publications

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“…Furthermore, the calculations on the industrial plant need to be better linked to the temporal dynamics of the energy systems in which it is embedded, including changes in industrial demand, energy prices and the availability of hydrogen. A more sophisticated sensitivity analysis with a better characterization of uncertainty ranges of the input parameters would improve the quality of the results and be useful input for strategic planning [50].…”

Section: Discussion Of Method Data Quality and Resultsmentioning

confidence: 99%

Assessing deployment pathways for greenhouse gas emissions reductions in an industrial plant – A case study for a complex oil refinery

et al. 2019

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Method to assess pathways for greenhouse gas emissions reductions for industrial plants. • Method successfully demonstrated for a large, complex oil refinery in Europe. • We examined energy efficiency, carbon capture and storage, biomass gasification and pyrolysis. • Pathway with energy efficiency and BIG-CCS is most cost effective and shows deep emissions reductions. • However, ranking of pathways in terms of costs depends strongly on energy prices.

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Section: Discussion Of Method Data Quality and Resultsmentioning

confidence: 99%

Assessing deployment pathways for greenhouse gas emissions reductions in an industrial plant – A case study for a complex oil refinery

et al. 2019

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“…For the entire RES, more than 34% of electricity was insufficient and imported from adjacent power grids. Additionally, among the domestic RES electricity-supply patterns, fossil-based power occupied more than 95% while In addition, one of problems for RES planning faced by decision makers is the multi-uncertainty associated with various characteristic features in the study system [39,40] , , , , , ,…”

Section: Case Studymentioning

confidence: 99%

A copula-based flexible-stochastic programming method for planning regional energy system under multiple uncertainties: A case study of the urban agglomeration of Beijing and Tianjin

Huang

et al. 2018

Applied Energy

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In this study, a copula-based flexible-stochastic programming (CFSP) method is developed for planning regional energy system (RES). CFSP can deal with multiple uncertainties expressed as interval values, random variables and fuzzy sets as well as their combinations employed to objective function and soft constraints. It can also reflect uncertain interactions among random variables through using copula functions even having different probability distributions and previously unknown correlations. Then, based on the developed CFSP approach, a CFSP-RES model is formulated for planning RES of the urban agglomeration of Beijing and Tianjin (China). Results disclose that uncertainties existed in the system components have significant effects on the outputs of decision variables and system cost, and the variation of system cost is reached 16.3%. Results also reveal that air pollutant emissions can be mitigated if the urban agglomeration can co-implement renewable energy development plans (REDP) over the planning horizon, with the reductive rates of [3.3, 7.6] % of sulfur dioxide (SO2), [2.7, 4.1] % of nitrogen oxides (NOx) and [7.0, 11.5] % of particulate matter (PM10). Compared to joint-probabilistic chance-constrained programming (JCP), the CFSP method is more effective for handling multiple random parameters associated with different probability distributions in which their correlations are unknown. Thus, it is not limited to some unjustified assumptions and can be applied to a wider range of problems than previous studies. The findings are helpful to explore the influence of interaction among random variables on modeling outputs and provide in-depth analysis for identifying desired decision schemes for planning RES.

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“…In this sense, Moret et al [42] present a method to characterise input uncertainty for strategic energy planning models. This method proposes the definition of a set of criteria for the establishment of variation ranges for uncertain parameters, instead of the definition of pdfs.…”

Section: Model Input Uncertainty and Monte Carlo Simulation Set-upmentioning

confidence: 99%

“…[42] present a literature review about characterisation of input uncertainties in strategic energy planning models. They found that, in the context of strategic design problems, Tock and Maréchal [43] use normal, uniform and beta distributions to define uncertainty of economic parameters with lower and upper limits defined from institutional reports.…”

Section: Model Input Uncertainty and Monte Carlo Simulation Set-upmentioning

confidence: 99%

stUPscales: An R-Package for Spatio-Temporal Uncertainty Propagation across Multiple Scales with Examples in Urban Water Modelling

Torres-Matallana

Leopold

Heuvelink

2018

Water

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Integrated environmental modelling requires coupling sub-models at different spatial and temporal scales, thus accounting for change of support procedures (aggregation and disaggregation). We introduce the R-package spatio-temporal Uncertainty Propagation across multiple scales, stUPscales, which constitutes a contribution to state-of-the-art open source tools that support uncertainty propagation analysis in temporal and spatio-temporal domains. We illustrate the tool with an uncertainty propagation example in environmental modelling, specifically in the urban water domain. The functionalities of the class setup and the methods and functions MC.setup, MC.sim, MC.analysis and Agg.t are explained, which are used for setting up, running and analysing Monte Carlo uncertainty propagation simulations, and for spatio-temporal aggregation. We also show how the package can be used to model and predict variables that vary in space and time by using a spatio-temporal variogram model and space-time ordinary kriging. stUPscales takes uncertainty characterisation and propagation a step further by including temporal and spatio-temporal auto-and cross-correlation, resulting in more realistic (spatio-)temporal series of environmental variables. Due to its modularity, the package allows the implementation of additional methods and functions for spatio-temporal disaggregation of model inputs and outputs, when linking models across multiple space-time scales.

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Characterization of input uncertainties in strategic energy planning models

Cited by 111 publications

References 42 publications

Assessing deployment pathways for greenhouse gas emissions reductions in an industrial plant – A case study for a complex oil refinery

Assessing deployment pathways for greenhouse gas emissions reductions in an industrial plant – A case study for a complex oil refinery

A copula-based flexible-stochastic programming method for planning regional energy system under multiple uncertainties: A case study of the urban agglomeration of Beijing and Tianjin

stUPscales: An R-Package for Spatio-Temporal Uncertainty Propagation across Multiple Scales with Examples in Urban Water Modelling

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