IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v148y2015icp334-347.html
   My bibliography  Save this article

Modeling of non-linear CHP efficiency curves in distributed energy systems

Author

Listed:
  • Milan, Christian
  • Stadler, Michael
  • Cardoso, Gonçalo
  • Mashayekh, Salman

Abstract

Distributed energy resources gain an increased importance in commercial and industrial building design. Combined heat and power (CHP) units are considered as one of the key technologies for cost and emission reduction in buildings. In order to make optimal decisions on investment and operation for these technologies, detailed system models are needed. These models are often formulated as linear programming problems to keep computational costs and complexity in a reasonable range. However, CHP systems involve variations of the efficiency for large nameplate capacity ranges and in case of part load operation, which can be even of non-linear nature. Since considering these characteristics would turn the models into non-linear problems, in most cases only constant efficiencies are assumed. This paper proposes possible solutions to address this issue. For a mixed integer linear programming problem two approaches are formulated using binary and Special-Ordered-Set (SOS) variables. Both suggestions have been implemented into the optimization model DER–CAM to simulate investment decisions of CHP micro-turbines and CHP fuel cells with variable efficiencies. The approaches have further been applied successfully in a case study with four different commercial buildings. Comparison of the results between the standard version and the new approaches indicate that total annual system costs remain almost unchanged. System performance is subject to change and storage technologies become more important. Part load operation has mainly been found important for fuel cell units. The micro-turbine is found almost exclusively in full load, thus rendering the application of the new approaches for this technology unnecessary for the considered unit sizes and building types. The approach using binary variables was the most promising method to model variable efficiencies in terms of computational costs and results. It should especially be considered for specific fuel cell technologies. Further investigation on the impacts of this approach on the prediction of fuel cell and micro-turbine performance is suggested.

Suggested Citation

  • Milan, Christian & Stadler, Michael & Cardoso, Gonçalo & Mashayekh, Salman, 2015. "Modeling of non-linear CHP efficiency curves in distributed energy systems," Applied Energy, Elsevier, vol. 148(C), pages 334-347.
    • Handle: RePEc:eee:appene:v:148:y:2015:i:c:p:334-347
    DOI: 10.1016/j.apenergy.2015.03.053
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261915003372
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2015.03.053?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Münster, Marie & Morthorst, Poul Erik & Larsen, Helge V. & Bregnbæk, Lars & Werling, Jesper & Lindboe, Hans Henrik & Ravn, Hans, 2012. "The role of district heating in the future Danish energy system," Energy, Elsevier, vol. 48(1), pages 47-55.
    2. Ren, Hongbo & Gao, Weijun, 2010. "A MILP model for integrated plan and evaluation of distributed energy systems," Applied Energy, Elsevier, vol. 87(3), pages 1001-1014, March.
    3. Hawkes, A.D. & Leach, M.A., 2009. "Modelling high level system design and unit commitment for a microgrid," Applied Energy, Elsevier, vol. 86(7-8), pages 1253-1265, July.
    4. Thorin, Eva & Brand, Heike & Weber, Christoph, 2005. "Long-term optimization of cogeneration systems in a competitive market environment," Applied Energy, Elsevier, vol. 81(2), pages 152-169, June.
    5. 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.
    6. Milan, Christian & Bojesen, Carsten & Nielsen, Mads Pagh, 2012. "A cost optimization model for 100% renewable residential energy supply systems," Energy, Elsevier, vol. 48(1), pages 118-127.
    7. Stadler, M. & Kloess, M. & Groissböck, M. & Cardoso, G. & Sharma, R. & Bozchalui, M.C. & Marnay, C., 2013. "Electric storage in California’s commercial buildings," Applied Energy, Elsevier, vol. 104(C), pages 711-722.
    8. Liu, Pei & Pistikopoulos, Efstratios N. & Li, Zheng, 2010. "An energy systems engineering approach to the optimal design of energy systems in commercial buildings," Energy Policy, Elsevier, vol. 38(8), pages 4224-4231, August.
    9. Cardoso, G. & Stadler, M. & Bozchalui, M.C. & Sharma, R. & Marnay, C. & Barbosa-Póvoa, A. & Ferrão, P., 2014. "Optimal investment and scheduling of distributed energy resources with uncertainty in electric vehicle driving schedules," Energy, Elsevier, vol. 64(C), pages 17-30.
    10. Lund, H. & Möller, B. & Mathiesen, B.V. & Dyrelund, A., 2010. "The role of district heating in future renewable energy systems," Energy, Elsevier, vol. 35(3), pages 1381-1390.
    11. Cho, Heejin & Mago, Pedro J. & Luck, Rogelio & Chamra, Louay M., 2009. "Evaluation of CCHP systems performance based on operational cost, primary energy consumption, and carbon dioxide emission by utilizing an optimal operation scheme," Applied Energy, Elsevier, vol. 86(12), pages 2540-2549, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cedillos Alvarado, Dagoberto & Acha, Salvador & Shah, Nilay & Markides, Christos N., 2016. "A Technology Selection and Operation (TSO) optimisation model for distributed energy systems: Mathematical formulation and case study," Applied Energy, Elsevier, vol. 180(C), pages 491-503.
    2. Flores, Robert J. & Brouwer, Jacob, 2018. "Optimal design of a distributed energy resource system that economically reduces carbon emissions," Applied Energy, Elsevier, vol. 232(C), pages 119-138.
    3. Mathiesen, B.V. & Lund, H. & Connolly, D. & Wenzel, H. & Østergaard, P.A. & Möller, B. & Nielsen, S. & Ridjan, I. & Karnøe, P. & Sperling, K. & Hvelplund, F.K., 2015. "Smart Energy Systems for coherent 100% renewable energy and transport solutions," Applied Energy, Elsevier, vol. 145(C), pages 139-154.
    4. Naraharisetti, Pavan Kumar & Karimi, I.A. & Anand, Abhay & Lee, Dong-Yup, 2011. "A linear diversity constraint – Application to scheduling in microgrids," Energy, Elsevier, vol. 36(7), pages 4235-4243.
    5. Omu, Akomeno & Choudhary, Ruchi & Boies, Adam, 2013. "Distributed energy resource system optimisation using mixed integer linear programming," Energy Policy, Elsevier, vol. 61(C), pages 249-266.
    6. Dominković, D.F. & Weinand, J.M. & Scheller, F. & D'Andrea, M. & McKenna, R., 2022. "Reviewing two decades of energy system analysis with bibliometrics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    7. Morvaj, Boran & Evins, Ralph & Carmeliet, Jan, 2017. "Decarbonizing the electricity grid: The impact on urban energy systems, distribution grids and district heating potential," Applied Energy, Elsevier, vol. 191(C), pages 125-140.
    8. Morvaj, Boran & Evins, Ralph & Carmeliet, Jan, 2016. "Optimization framework for distributed energy systems with integrated electrical grid constraints," Applied Energy, Elsevier, vol. 171(C), pages 296-313.
    9. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "Uncertainty and global sensitivity analysis for the optimal design of distributed energy systems," Applied Energy, Elsevier, vol. 214(C), pages 219-238.
    10. Cardoso, G. & Stadler, M. & Mashayekh, S. & Hartvigsson, E., 2017. "The impact of ancillary services in optimal DER investment decisions," Energy, Elsevier, vol. 130(C), pages 99-112.
    11. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    12. Saumweber, Andrea & Wederhake, Lars & Cardoso, Gonçalo & Fridgen, Gilbert & Heleno, Miguel, 2021. "Designing Pareto optimal electricity retail rates when utility customers are prosumers," Energy Policy, Elsevier, vol. 156(C).
    13. Cardoso, Gonçalo & Brouhard, Thomas & DeForest, Nicholas & Wang, Dai & Heleno, Miguel & Kotzur, Leander, 2018. "Battery aging in multi-energy microgrid design using mixed integer linear programming," Applied Energy, Elsevier, vol. 231(C), pages 1059-1069.
    14. Sorknæs, Peter & Lund, Henrik & Andersen, Anders N., 2015. "Future power market and sustainable energy solutions – The treatment of uncertainties in the daily operation of combined heat and power plants," Applied Energy, Elsevier, vol. 144(C), pages 129-138.
    15. Ghatikar, Girish & Mashayekh, Salman & Stadler, Michael & Yin, Rongxin & Liu, Zhenhua, 2016. "Distributed energy systems integration and demand optimization for autonomous operations and electric grid transactions," Applied Energy, Elsevier, vol. 167(C), pages 432-448.
    16. Bazmi, Aqeel Ahmed & Zahedi, Gholamreza, 2011. "Sustainable energy systems: Role of optimization modeling techniques in power generation and supply—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(8), pages 3480-3500.
    17. Karlsson, Kenneth B. & Petrović, Stefan N. & Næraa, Rikke, 2016. "Heat supply planning for the ecological housing community Munksøgård," Energy, Elsevier, vol. 115(P3), pages 1733-1747.
    18. Sovacool, Benjamin K. & Martiskainen, Mari, 2020. "Hot transformations: Governing rapid and deep household heating transitions in China, Denmark, Finland and the United Kingdom," Energy Policy, Elsevier, vol. 139(C).
    19. Chen, Yen-Haw & Lu, Su-Ying & Chang, Yung-Ruei & Lee, Ta-Tung & Hu, Ming-Che, 2013. "Economic analysis and optimal energy management models for microgrid systems: A case study in Taiwan," Applied Energy, Elsevier, vol. 103(C), pages 145-154.
    20. Kuang, Yanqing & Chen, Yang & Hu, Mengqi & Yang, Dong, 2017. "Influence analysis of driver behavior and building category on economic performance of electric vehicle to grid and building integration," Applied Energy, Elsevier, vol. 207(C), pages 427-437.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:148:y:2015:i:c:p:334-347. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.