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

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, in...

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Published inEnergies (Basel) Vol. 12; no. 20; p. 4006
Main Authors Lopion, Peter, Markewitz, Peter, Stolten, Detlef, Robinius, Martin
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 21.10.2019
Subjects
Alternative energy sources
Carbon
Climate change
Costs
Emissions
Energy industry
energy system modeling
Industrial plant emissions
Linear programming
Monte Carlo simulation
Optimization
Paris Agreement
penny switching effect
Power plants
Process engineering
Renewable resources
robustness
Turbines
uncertainties
Wind power
United Kingdom > UK
Belgium
Germany
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Abstract 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.
AbstractList 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.
The approach described in this paper focuses on the latter aspects. Aside from the impact on the results, it is important to take the changes in computation time using the QP approach into account. Because the computational effort for such complex models is affected by various aspects, the change in computation time is dependent on the individual model, its input parameters, the solver, and its setup. Instead of the penny switching effects in the LP model, the technology shares are affected much less by a variation in the specific investment costs. [...]the QP or mixed-integer quadratic programming (MIQP) approach to investment costs represents an alternative to cost minimization in conventional LP or mixed-integer linear programming (MILP) models.
Author Robinius, Martin
Stolten, Detlef
Markewitz, Peter
Lopion, Peter
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Snippet Designing the future energy supply in accordance with ambitious climate change mitigation goals is a challenging issue. Common tools for planning and...
The approach described in this paper focuses on the latter aspects. Aside from the impact on the results, it is important to take the changes in computation...
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SubjectTerms Alternative energy sources
Carbon
Climate change
Costs
Emissions
Energy industry
energy system modeling
Industrial plant emissions
Linear programming
Monte Carlo simulation
Optimization
Paris Agreement
penny switching effect
Power plants
Process engineering
Renewable resources
robustness
Turbines
uncertainties
Wind power
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Title Cost Uncertainties in Energy System Optimization Models: A Quadratic Programming Approach for Avoiding Penny Switching Effects
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