scholarly journals Use of Online Energy System Optimization Models

10.5772/18931 ◽  
2011 ◽  
Author(s):  
Diego Ruiz ◽  
Carlos Ruiz
Keyword(s):  
System Optimization ◽  
Energy System ◽  
Optimization Models

High performance computing for energy system optimization models: Enhancing the energy policy tool kit

Energy Policy ◽  
2019 ◽  
Vol 128 ◽  
pp. 66-74 ◽  
Author(s):  
Tarun Sharma ◽  
James Glynn ◽  
Evangelos Panos ◽  
Paul Deane ◽  
Maurizio Gargiulo ◽  
...  
Keyword(s):  
High Performance Computing ◽  
Energy Policy ◽  
High Performance ◽  
System Optimization ◽  
Energy System ◽  
Optimization Models ◽  
Policy Tool ◽  
Performance Computing

Enhancing policy realism in energy system optimization models: Politically feasible decarbonization pathways for the United States

Energy Policy ◽  
2022 ◽  
Vol 161 ◽  
pp. 112754
Author(s):  
Qianru Zhu ◽  
Benjamin D. Leibowicz ◽  
Joshua W. Busby ◽  
Sarang Shidore ◽  
David E. Adelman ◽  
...  
Keyword(s):  
United States ◽  
System Optimization ◽  
Energy System ◽  
The United States ◽  
Optimization Models

Integration of groundwater heat pumps into energy system optimization models

Energy ◽  
2021 ◽  
pp. 121607
Author(s):  
Smajil Halilovic ◽  
Leonhard Odersky ◽  
Thomas Hamacher
Keyword(s):  
System Optimization ◽  
Energy System ◽  
Heat Pumps ◽  
Optimization Models ◽  
Groundwater Heat Pumps

scholarly journals Classification and Evaluation of Concepts for Improving the Performance of Applied Energy System Optimization Models

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4656 ◽  
Author(s):  
Karl-Kiên Cao ◽  
Kai von Krbek ◽  
Manuel Wetzel ◽  
Felix Cebulla ◽  
Sebastian Schreck
Keyword(s):  
Model Reduction ◽  
Mathematical Problem ◽  
Real Data ◽  
System Optimization ◽  
Energy System ◽  
Optimization Models ◽  
Systematic Evaluation ◽  
Problem Size ◽  
Energy System Model ◽  
Speed Up

Energy system optimization models used for capacity expansion and dispatch planning are established tools for decision-making support in both energy industry and energy politics. The ever-increasing complexity of the systems under consideration leads to an increase in mathematical problem size of the models. This implies limitations of today’s common solution approaches especially with regard to required computing times. To tackle this challenge many model-based speed-up approaches exist which, however, are typically only demonstrated on small generic test cases. In addition, in applied energy systems analysis the effects of such approaches are often not well understood. The novelty of this study is the systematic evaluation of several model reduction and heuristic decomposition techniques for a large applied energy system model using real data and particularly focusing on reachable speed-up. The applied model is typically used for examining German energy scenarios and allows expansion of storage and electricity transmission capacities. We find that initial computing times of more than two days can be reduced up to a factor of ten while having acceptable loss of accuracy. Moreover, we explain what we mean by “effectiveness of model reduction” which limits the possible speed-up with shared memory computers used in this study.

scholarly journals A review of approaches to uncertainty assessment in energy system optimization models

2018 ◽  
Vol 21 ◽  
pp. 204-217 ◽  
Author(s):  
Xiufeng Yue ◽  
Steve Pye ◽  
Joseph DeCarolis ◽  
Francis G.N. Li ◽  
Fionn Rogan ◽  
...  
Keyword(s):  
System Optimization ◽  
Energy System ◽  
Uncertainty Assessment ◽  
Optimization Models

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

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 4006 ◽  
Author(s):  
Lopion ◽  
Markewitz ◽  
Stolten ◽  
Robinius
Keyword(s):  
Quadratic Programming ◽  
Cost Optimization ◽  
Computation Time ◽  
System Optimization ◽  
Energy System ◽  
Optimization Models ◽  
Programming Approach ◽  
Sustainable Technologies ◽  
Specific Investment ◽  
Installed Capacity

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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