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Optimal planning of thermal energy systems in a microgrid with seasonal storage and piecewise affine cost functions

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  • Mansoor, Muhammad
  • Stadler, Michael
  • Zellinger, Michael
  • Lichtenegger, Klaus
  • Auer, Hans
  • Cosic, Armin

Abstract

The optimal design of microgrids with thermal energy system requires optimization techniques that can provide investment and scheduling of the technology portfolio involved. In the modeling of such systems with seasonal storage capability, the two main challenges include the low temporal resolution of available data and the non-linear cost versus capacity relationship of solar thermal and heat storage technologies. This work overcomes these challenges by developing two different optimization models based on mixed-integer linear programming with objectives to minimize the total energy costs and carbon dioxide emissions. Piecewise affine functions are used to approximate the non-linear cost versus capacity behavior. The developed methods are applied to the optimal planning of a case study in Austria. The results of the models are compared based on the accuracy and real-time performance together with the impact of piecewise affine cost functions versus non-piecewise affine fixed cost functions. The results show that the investment decisions of both models are in good agreement with each other while the computational time for the 8760-h based model is significantly greater than the model having three representative periods. The models with piecewise affine cost functions show larger capacities of technologies than non-piecewise affine fixed cost function based models.

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  • Mansoor, Muhammad & Stadler, Michael & Zellinger, Michael & Lichtenegger, Klaus & Auer, Hans & Cosic, Armin, 2021. "Optimal planning of thermal energy systems in a microgrid with seasonal storage and piecewise affine cost functions," Energy, Elsevier, vol. 215(PA).
    • Handle: RePEc:eee:energy:v:215:y:2021:i:pa:s0360544220322027
    DOI: 10.1016/j.energy.2020.119095
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    as
    1. Ma, Zhiwei & Bao, Huashan & Roskilly, Anthony Paul, 2019. "Seasonal solar thermal energy storage using thermochemical sorption in domestic dwellings in the UK," Energy, Elsevier, vol. 166(C), pages 213-222.
    2. Launay, S. & Kadoch, B. & Le Métayer, O. & Parrado, C., 2019. "Analysis strategy for multi-criteria optimization: Application to inter-seasonal solar heat storage for residential building needs," Energy, Elsevier, vol. 171(C), pages 419-434.
    3. 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.
    4. Nahmmacher, Paul & Schmid, Eva & Hirth, Lion & Knopf, Brigitte, 2016. "Carpe diem: A novel approach to select representative days for long-term power system modeling," Energy, Elsevier, vol. 112(C), pages 430-442.
    5. 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.
    6. Stadler, M. & Groissböck, M. & Cardoso, G. & Marnay, C., 2014. "Optimizing Distributed Energy Resources and building retrofits with the strategic DER-CAModel," Applied Energy, Elsevier, vol. 132(C), pages 557-567.
    7. Moser, Simon & Mayrhofer, Julia & Schmidt, Ralf-Roman & Tichler, Robert, 2018. "Socioeconomic cost-benefit-analysis of seasonal heat storages in district heating systems with industrial waste heat integration," Energy, Elsevier, vol. 160(C), pages 868-874.
    8. Mashayekh, Salman & Stadler, Michael & Cardoso, Gonçalo & Heleno, Miguel, 2017. "A mixed integer linear programming approach for optimal DER portfolio, sizing, and placement in multi-energy microgrids," Applied Energy, Elsevier, vol. 187(C), pages 154-168.
    9. Li, Tingxian & Wang, Ruzhu & Kiplagat, Jeremiah K. & Kang, YongTae, 2013. "Performance analysis of an integrated energy storage and energy upgrade thermochemical solid–gas sorption system for seasonal storage of solar thermal energy," Energy, Elsevier, vol. 50(C), pages 454-467.
    10. Pfenninger, Stefan, 2017. "Dealing with multiple decades of hourly wind and PV time series in energy models: A comparison of methods to reduce time resolution and the planning implications of inter-annual variability," Applied Energy, Elsevier, vol. 197(C), pages 1-13.
    11. Pinel, Patrice & Cruickshank, Cynthia A. & Beausoleil-Morrison, Ian & Wills, Adam, 2011. "A review of available methods for seasonal storage of solar thermal energy in residential applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(7), pages 3341-3359, September.
    12. Böhm, Hans & Lindorfer, Johannes, 2019. "Techno-economic assessment of seasonal heat storage in district heating with thermochemical materials," Energy, Elsevier, vol. 179(C), pages 1246-1264.
    13. Schütz, Thomas & Schiffer, Lutz & Harb, Hassan & Fuchs, Marcus & Müller, Dirk, 2017. "Optimal design of energy conversion units and envelopes for residential building retrofits using a comprehensive MILP model," Applied Energy, Elsevier, vol. 185(P1), pages 1-15.
    14. Liu, Long & Zhu, Neng & Zhao, Jing, 2016. "Thermal equilibrium research of solar seasonal storage system coupling with ground-source heat pump," Energy, Elsevier, vol. 99(C), pages 83-90.
    15. Xu, J. & Li, Y. & Wang, R.Z. & Liu, W., 2014. "Performance investigation of a solar heating system with underground seasonal energy storage for greenhouse application," Energy, Elsevier, vol. 67(C), pages 63-73.
    16. Mehleri, E.D. & Sarimveis, H. & Markatos, N.C. & Papageorgiou, L.G., 2013. "Optimal design and operation of distributed energy systems: Application to Greek residential sector," Renewable Energy, Elsevier, vol. 51(C), pages 331-342.
    17. 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.
    18. Köfinger, M. & Schmidt, R.R. & Basciotti, D. & Terreros, O. & Baldvinsson, I. & Mayrhofer, J. & Moser, S. & Tichler, R. & Pauli, H., 2018. "Simulation based evaluation of large scale waste heat utilization in urban district heating networks: Optimized integration and operation of a seasonal storage," Energy, Elsevier, vol. 159(C), pages 1161-1174.
    19. Ciampi, Giovanni & Rosato, Antonio & Sibilio, Sergio, 2018. "Thermo-economic sensitivity analysis by dynamic simulations of a small Italian solar district heating system with a seasonal borehole thermal energy storage," Energy, Elsevier, vol. 143(C), pages 757-771.
    20. Steen, David & Stadler, Michael & Cardoso, Gonçalo & Groissböck, Markus & DeForest, Nicholas & Marnay, Chris, 2015. "Modeling of thermal storage systems in MILP distributed energy resource models," Applied Energy, Elsevier, vol. 137(C), pages 782-792.
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    4. Joel Alpízar-Castillo & Laura Ramirez-Elizondo & Pavol Bauer, 2022. "Assessing the Role of Energy Storage in Multiple Energy Carriers toward Providing Ancillary Services: A Review," Energies, MDPI, vol. 16(1), pages 1-31, December.
    5. Hering, Dominik & Xhonneux, André & Müller, Dirk, 2021. "Design optimization of a heating network with multiple heat pumps using mixed integer quadratically constrained programming," Energy, Elsevier, vol. 226(C).
    6. Ikäheimo, Jussi & Weiss, Robert & Kiviluoma, Juha & Pursiheimo, Esa & Lindroos, Tomi J., 2022. "Impact of power-to-gas on the cost and design of the future low-carbon urban energy system," Applied Energy, Elsevier, vol. 305(C).
    7. Hering, Dominik & Faller, Michael R. & Xhonneux, André & Müller, Dirk, 2022. "Operational optimization of a 4th generation district heating network with mixed integer quadratically constrained programming," Energy, Elsevier, vol. 250(C).
    8. Muschick, D. & Zlabinger, S. & Moser, A. & Lichtenegger, K. & Gölles, M., 2022. "A multi-layer model of stratified thermal storage for MILP-based energy management systems," Applied Energy, Elsevier, vol. 314(C).
    9. Jieran Feng & Hao Zhou, 2022. "Bi-Level Optimal Capacity Planning of Load-Side Electric Energy Storage Using an Emission-Considered Carbon Incentive Mechanism," Energies, MDPI, vol. 15(13), pages 1-18, June.
    10. Akulker, Handan & Aydin, Erdal, 2023. "Optimal design and operation of a multi-energy microgrid using mixed-integer nonlinear programming: Impact of carbon cap and trade system and taxing on equipment selections," Applied Energy, Elsevier, vol. 330(PA).

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