Efficient and equitable spatial allocation of renewable power plants at the country scale

Drechsler, Martín; Egerer, Jonas; Lange, Martin; Masurowski, Frank; Meyerhoff, Jürgen; Oehlmann, Malte

doi:10.1038/nenergy.2017.124

Cited by 149 publications

(77 citation statements)

References 25 publications

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“…[1] In particular,w ind and solar power has increased tenfold globally from 2004 to 2014. [1] In particular,w ind and solar power has increased tenfold globally from 2004 to 2014.…”

Section: Biogasmentioning

confidence: 99%

The Potential of Biogas; the Solution to Energy Storage

et al. 2019

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“…[1] In particular,w ind and solar power has increased tenfold globally from 2004 to 2014. [1] In particular,w ind and solar power has increased tenfold globally from 2004 to 2014.…”

Section: Biogasmentioning

confidence: 99%

The Potential of Biogas; the Solution to Energy Storage

et al. 2019

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“…On the one hand, economies of scale, as well as the resulting increase in manufacturing efficiency, have been the major influencing factors on renewable energy costs over the last decades. Other important factors that impact the actual costs of an individual technology or project relate to market pricing, auction designs, technological developments, and geographical aspects, such as ground conditions or local construction and transport costs [24][25][26]. The approach described in this paper focuses on the latter aspects.…”

Section: Investment Cost Analysismentioning

confidence: 99%

Cost Uncertainties in Energy System Optimization Models: A Quadratic Programming Approach for Avoiding Penny Switching Effects

et al. 2019

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Designing the future energy supply in accordance with ambitious climate change mitigation goals is a challenging issue. Common tools for planning and calculating future investments in renewable and sustainable technologies are often linear energy system models based on cost optimization. However, input data and the underlying assumptions of future developments are subject to uncertainties that negatively affect the robustness of results. This paper introduces a quadratic programming approach to modifying linear, bottom-up energy system optimization models to take cost uncertainties into account. This is accomplished by implementing specific investment costs as a function of the installed capacity of each technology. In contrast to established approaches such as stochastic programming or Monte Carlo simulation, the computation time of the quadratic programming approach is only slightly higher than that of linear programming. The model’s outcomes were found to show a wider range as well as a more robust allocation of the considered technologies than the linear model equivalent.

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“…Other important factors that impact the actual costs of an individual technology or project relate to market pricing, auction designs, technological developments and geographical aspects. [21][22][23] The approach described in this paper focuses on the latter aspects. For that reason, the example of onshore wind turbines is selected for this following analysis.…”

Section: Investment Cost Analysismentioning

confidence: 99%

Cost Uncertainties in Energy System Optimisation Models: A Quadratic Programming Approach for Avoiding Penny Switching Effects

Lopion¹,

Markewitz²,

Stolten³

et al. 2019

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Designing the future energy supply in accordance with ambitious climate change mitigation goals is a challenging issue. Common tools for planning and calculating future investments in renewable and sustainable technologies are often linear energy system models based on cost optimisation. However, input data and the underlying assumptions of future developments are subject to uncertainties that negatively affect the robustness of results. This paper introduces a quadratic programming approach to modifying linear, bottom-up energy system optimisation models in order to take cost uncertainties into account. This is accomplished by implementing specific investment costs as a function of the installed capacity of each technology. In contrast to established approaches like stochastic programming or Monte Carlo Simulation, the computation time of the quadratic programming approach is only slightly higher than that of linear programming. The model’s outcomes were found to show a wider range as well as a more robust allocation of the considered technologies than the linear model equivalent.

show abstract

Efficient and equitable spatial allocation of renewable power plants at the country scale

Cited by 149 publications

References 25 publications

The Potential of Biogas; the Solution to Energy Storage

The Potential of Biogas; the Solution to Energy Storage

Cost Uncertainties in Energy System Optimization Models: A Quadratic Programming Approach for Avoiding Penny Switching Effects

Cost Uncertainties in Energy System Optimisation Models: A Quadratic Programming Approach for Avoiding Penny Switching Effects

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