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A multi-objective approach to determine time series aggregation strategies for optimal design of multi-energy systems

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  • Wang, Jing
  • Kang, Lixia
  • Liu, Yongzhong

Abstract

The optimal topology and operation of multi-energy systems (MES) can be determined by mathematical programming models, which are commonly difficult to solve due to the complex structures of the models and large-scale time-series data. The time series aggregation (TSA) strategies are usually adopted to reduce the complication of the data input and thus improve the solution efficiency. Nevertheless, for the optimal design of MES, significant differences exist in the structures and performances of the systems resulting from different TSA strategies. It is crucially important to determine appropriate TSA strategies in the optimal design of MES. In this work, a two-step method was proposed to determine TSA strategies based on a multi-objective optimization framework. In the first step, the preliminary design for the multi-energy system was obtained by different TSA strategies for the optimal design and operation of MES. In the second step, the total annual cost and loss of load probability (LSLP) of the system were taken as two objectives, and a multi-objective optimization approach was established to determine the appropriate TSA strategy for the optimal design of the MES, where the structure and performance of the system were considered simultaneously. The implementation and effectiveness of the proposed method were demonstrated by the optimal design of an MES with energy storage. The deviations of the design scheme and system performance obtained by different TSA strategies were comprehensively compared and analyzed. The results indicate that the feasibility of the optimal design schemes of the MES can be verified by using the original full-time series data, and the deviation of the results obtained by different TSA strategies can be quantified. The reduction of LSLP is at the expense of the increase of the costs. In the multiple time grids strategy (TG1) with the number of time steps (TS) 730, the TAC of the system increases from $9.79 × 107 to $1.56 × 108 when the LSLP of the system decreases from 7.90% to zero. The determination of the strategy based on the result of the minimum LSLP is critical to meet the demand in the energy system. The appropriate TSA strategies for the optimal design of MES can be selected and determined in compliance with specific requirements.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:258:y:2022:i:c:s0360544222016863
    DOI: 10.1016/j.energy.2022.124783
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    1. 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.
    2. de Guibert, Paul & Shirizadeh, Behrang & Quirion, Philippe, 2020. "Variable time-step: A method for improving computational tractability for energy system models with long-term storage," Energy, Elsevier, vol. 213(C).
    3. Shirizadeh, Behrang & Quirion, Philippe, 2022. "Do multi-sector energy system optimization models need hourly temporal resolution? A case study with an investment and dispatch model applied to France," Applied Energy, Elsevier, vol. 305(C).
    4. Gabrielli, Paolo & Gazzani, Matteo & Martelli, Emanuele & Mazzotti, Marco, 2018. "Optimal design of multi-energy systems with seasonal storage," Applied Energy, Elsevier, vol. 219(C), pages 408-424.
    5. Scott, Ian J. & Carvalho, Pedro M.S. & Botterud, Audun & Silva, Carlos A., 2019. "Clustering representative days for power systems generation expansion planning: Capturing the effects of variable renewables and energy storage," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    6. Renaldi, Renaldi & Friedrich, Daniel, 2017. "Multiple time grids in operational optimisation of energy systems with short- and long-term thermal energy storage," Energy, Elsevier, vol. 133(C), pages 784-795.
    7. Welder, Lara & Ryberg, D.Severin & Kotzur, Leander & Grube, Thomas & Robinius, Martin & Stolten, Detlef, 2018. "Spatio-temporal optimization of a future energy system for power-to-hydrogen applications in Germany," Energy, Elsevier, vol. 158(C), pages 1130-1149.
    8. Bahl, Björn & Kümpel, Alexander & Seele, Hagen & Lampe, Matthias & Bardow, André, 2017. "Time-series aggregation for synthesis problems by bounding error in the objective function," Energy, Elsevier, vol. 135(C), pages 900-912.
    9. 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.
    10. Priesmann, Jan & Nolting, Lars & Praktiknjo, Aaron, 2019. "Are complex energy system models more accurate? An intra-model comparison of power system optimization models," Applied Energy, Elsevier, vol. 255(C).
    11. Hassan, Rakibul & Das, Barun K. & Hasan, Mahmudul, 2022. "Integrated off-grid hybrid renewable energy system optimization based on economic, environmental, and social indicators for sustainable development," Energy, Elsevier, vol. 250(C).
    12. Göke, Leonard & Kendziorski, Mario, 2022. "Adequacy of time-series reduction for renewable energy systems," Energy, Elsevier, vol. 238(PA).
    13. Pfenninger, Stefan & Staffell, Iain, 2016. "Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data," Energy, Elsevier, vol. 114(C), pages 1251-1265.
    14. Buchholz, Stefanie & Gamst, Mette & Pisinger, David, 2020. "Sensitivity analysis of time aggregation techniques applied to capacity expansion energy system models," Applied Energy, Elsevier, vol. 269(C).
    15. Jann Michael Weinand & Fabian Scheller & Russell McKenna, 2020. "Reviewing energy system modelling of decentralized energy autonomy," Papers 2011.05915, arXiv.org.
    16. Klemm, Christian & Vennemann, Peter, 2021. "Modeling and optimization of multi-energy systems in mixed-use districts: A review of existing methods and approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    17. Weinand, Jann Michael & Scheller, Fabian & McKenna, Russell, 2020. "Reviewing energy system modelling of decentralized energy autonomy," Energy, Elsevier, vol. 203(C).
    18. Demirhan, C. Doga & Tso, William W. & Powell, Joseph B. & Pistikopoulos, Efstratios N., 2021. "A multi-scale energy systems engineering approach towards integrated multi-product network optimization," Applied Energy, Elsevier, vol. 281(C).
    19. 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).
    20. Weinand, Jann & Scheller, Fabian Johannes & McKenna, Russell, 2020. "Reviewing energy system modelling of decentralized energy autonomy," Working Paper Series in Production and Energy 41, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    21. Wang, Jing & Kang, Lixia & Huang, Xiankun & Liu, Yongzhong, 2021. "An analysis framework for quantitative evaluation of parametric uncertainty in a cooperated energy storage system with multiple energy carriers," Energy, Elsevier, vol. 226(C).
    22. van der Heijde, Bram & Vandermeulen, Annelies & Salenbien, Robbe & Helsen, Lieve, 2019. "Representative days selection for district energy system optimisation: a solar district heating system with seasonal storage," Applied Energy, Elsevier, vol. 248(C), pages 79-94.
    23. 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).
    24. Staffell, Iain & Pfenninger, Stefan, 2016. "Using bias-corrected reanalysis to simulate current and future wind power output," Energy, Elsevier, vol. 114(C), pages 1224-1239.
    25. 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).
    26. Gonzato, Sebastian & Bruninx, Kenneth & Delarue, Erik, 2021. "Long term storage in generation expansion planning models with a reduced temporal scope," Applied Energy, Elsevier, vol. 298(C).
    27. Olsen, Karen Pardos & Zong, Yi & You, Shi & Bindner, Henrik & Koivisto, Matti & Gea-Bermúdez, Juan, 2020. "Multi-timescale data-driven method identifying flexibility requirements for scenarios with high penetration of renewables," Applied Energy, Elsevier, vol. 264(C).
    28. Guelpa, Elisa & Bischi, Aldo & Verda, Vittorio & Chertkov, Michael & Lund, Henrik, 2019. "Towards future infrastructures for sustainable multi-energy systems: A review," Energy, Elsevier, vol. 184(C), pages 2-21.
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