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Time-series aggregation for synthesis problems by bounding error in the objective function

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  1. Zhang, Chao & Lasaulce, Samson & Hennebel, Martin & Saludjian, Lucas & Panciatici, Patrick & Poor, H. Vincent, 2021. "Decision-making oriented clustering: Application to pricing and power consumption scheduling," Applied Energy, Elsevier, vol. 297(C).
  2. Teichgraeber, Holger & Lindenmeyer, Constantin P. & Baumgärtner, Nils & Kotzur, Leander & Stolten, Detlef & Robinius, Martin & Bardow, André & Brandt, Adam R., 2020. "Extreme events in time series aggregation: A case study for optimal residential energy supply systems," Applied Energy, Elsevier, vol. 275(C).
  3. Teichgraeber, Holger & Brodrick, Philip G. & Brandt, Adam R., 2017. "Optimal design and operations of a flexible oxyfuel natural gas plant," Energy, Elsevier, vol. 141(C), pages 506-518.
  4. Jing, Rui & Wang, Meng & Zhang, Zhihui & Wang, Xiaonan & Li, Ning & Shah, Nilay & Zhao, Yingru, 2019. "Distributed or centralized? Designing district-level urban energy systems by a hierarchical approach considering demand uncertainties," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
  5. Timo Kannengießer & Maximilian Hoffmann & Leander Kotzur & Peter Stenzel & Fabian Schuetz & Klaus Peters & Stefan Nykamp & Detlef Stolten & Martin Robinius, 2019. "Reducing Computational Load for Mixed Integer Linear Programming: An Example for a District and an Island Energy System," Energies, MDPI, vol. 12(14), pages 1-27, July.
  6. Maximilian Hoffmann & Leander Kotzur & Detlef Stolten & Martin Robinius, 2020. "A Review on Time Series Aggregation Methods for Energy System Models," Energies, MDPI, vol. 13(3), pages 1-61, February.
  7. Dong, Haoxin & Shan, Zijing & Zhou, Jianli & Xu, Chuanbo & Chen, Wenjun, 2023. "Refined modeling and co-optimization of electric-hydrogen-thermal-gas integrated energy system with hybrid energy storage," Applied Energy, Elsevier, vol. 351(C).
  8. Teichgraeber, Holger & Küpper, Lucas Elias & Brandt, Adam R., 2021. "Designing reliable future energy systems by iteratively including extreme periods in time-series aggregation," Applied Energy, Elsevier, vol. 304(C).
  9. Luo, Xianglong & Wei, Youxing & Qiu, Guanfu & Liang, Yingzong & Chen, Jianyong & Yang, Zhi & Wang, Chao & Chen, Ying, 2020. "Simultaneous design and off-design operation optimization of a waste heat-driven organic Rankine cycle using a multi-period mathematical programming method," Energy, Elsevier, vol. 213(C).
  10. Hoffmann, Maximilian & Kotzur, Leander & Stolten, Detlef, 2022. "The Pareto-optimal temporal aggregation of energy system models," Applied Energy, Elsevier, vol. 315(C).
  11. Tso, William W. & Demirhan, C. Doga & Heuberger, Clara F. & Powell, Joseph B. & Pistikopoulos, Efstratios N., 2020. "A hierarchical clustering decomposition algorithm for optimizing renewable power systems with storage," Applied Energy, Elsevier, vol. 270(C).
  12. Wang, Jing & Kang, Lixia & Liu, Yongzhong, 2022. "A multi-objective approach to determine time series aggregation strategies for optimal design of multi-energy systems," Energy, Elsevier, vol. 258(C).
  13. Bohlayer, Markus & Bürger, Adrian & Fleschutz, Markus & Braun, Marco & Zöttl, Gregor, 2021. "Multi-period investment pathways - Modeling approaches to design distributed energy systems under uncertainty," Applied Energy, Elsevier, vol. 285(C).
  14. Müller, Inga M., 2022. "Energy system modeling with aggregated time series: A profiling approach," Applied Energy, Elsevier, vol. 322(C).
  15. Yeganefar, Ali & Amin-Naseri, Mohammad Reza & Sheikh-El-Eslami, Mohammad Kazem, 2020. "Improvement of representative days selection in power system planning by incorporating the extreme days of the net load to take account of the variability and intermittency of renewable resources," Applied Energy, Elsevier, vol. 272(C).
  16. Baumgärtner, Nils & Delorme, Roman & Hennen, Maike & Bardow, André, 2019. "Design of low-carbon utility systems: Exploiting time-dependent grid emissions for climate-friendly demand-side management," Applied Energy, Elsevier, vol. 247(C), pages 755-765.
  17. Baumgärtner, Nils & Shu, David & Bahl, Björn & Hennen, Maike & Hollermann, Dinah Elena & Bardow, André, 2020. "DeLoop: Decomposition-based Long-term operational optimization of energy systems with time-coupling constraints," Energy, Elsevier, vol. 198(C).
  18. Teichgraeber, Holger & Brandt, Adam R., 2019. "Clustering methods to find representative periods for the optimization of energy systems: An initial framework and comparison," Applied Energy, Elsevier, vol. 239(C), pages 1283-1293.
  19. Brodrick, Philip G. & Brandt, Adam R. & Durlofsky, Louis J., 2018. "Optimal design and operation of integrated solar combined cycles under emissions intensity constraints," Applied Energy, Elsevier, vol. 226(C), pages 979-990.
  20. Pöstges, Arne & Weber, Christoph, 2023. "Identifying key elements for adequate simplifications of investment choices – The case of wind energy expansion," Energy Economics, Elsevier, vol. 120(C).
  21. Yokoyama, Ryohei & Takeuchi, Kotaro & Shinano, Yuji & Wakui, Tetsuya, 2021. "Effect of model reduction by time aggregation in multiobjective optimal design of energy supply systems by a hierarchical MILP method," Energy, Elsevier, vol. 228(C).
  22. Teichgraeber, Holger & Brandt, Adam R., 2022. "Time-series aggregation for the optimization of energy systems: Goals, challenges, approaches, and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
  23. Kleinebrahm, Max & Weinand, Jann Michael & Naber, Elias & McKenna, Russell & Ardone, Armin, 2023. "Analysing municipal energy system transformations in line with national greenhouse gas reduction strategies," Applied Energy, Elsevier, vol. 332(C).
  24. Kotzur, Leander & Markewitz, Peter & Robinius, Martin & Stolten, Detlef, 2018. "Time series aggregation for energy system design: Modeling seasonal storage," Applied Energy, Elsevier, vol. 213(C), pages 123-135.
  25. Michael Stadler & Zack Pecenak & Patrick Mathiesen & Kelsey Fahy & Jan Kleissl, 2020. "Performance Comparison between Two Established Microgrid Planning MILP Methodologies Tested On 13 Microgrid Projects," Energies, MDPI, vol. 13(17), pages 1-24, August.
  26. Arne Pöstges & Christoph Weber, "undated". "Identifying key elements for adequate simplifications of investment choices - The case of wind energy expansion," EWL Working Papers 2101, University of Duisburg-Essen, Chair for Management Science and Energy Economics.
  27. Yokoyama, Ryohei & Shinano, Yuji & Wakayama, Yuki & Wakui, Tetsuya, 2019. "Model reduction by time aggregation for optimal design of energy supply systems by an MILP hierarchical branch and bound method," Energy, Elsevier, vol. 181(C), pages 782-792.
  28. Hoffmann, Maximilian & Priesmann, Jan & Nolting, Lars & Praktiknjo, Aaron & Kotzur, Leander & Stolten, Detlef, 2021. "Typical periods or typical time steps? A multi-model analysis to determine the optimal temporal aggregation for energy system models," Applied Energy, Elsevier, vol. 304(C).
  29. Kuepper, Lucas Elias & Teichgraeber, Holger & Baumgärtner, Nils & Bardow, André & Brandt, Adam R., 2022. "Wind data introduce error in time-series reduction for capacity expansion modelling," Energy, Elsevier, vol. 256(C).
  30. Pecenak, Zachary K. & Stadler, Michael & Mathiesen, Patrick & Fahy, Kelsey & Kleissl, Jan, 2020. "Robust design of microgrids using a hybrid minimum investment optimization," Applied Energy, Elsevier, vol. 276(C).
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