IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v137y2021ics1364032120306882.html
   My bibliography  Save this article

Suitability assessment of models in the industrial energy system design

Author

Listed:
  • Urban, Kristof L.
  • Scheller, Fabian
  • Bruckner, Thomas

Abstract

This article reviews models in the scientific literature for industrial final-energy generation system design to evaluate their applicability in practice. Since energy efficiency has top priority worldwide and industry accounts for half of the world's energy consumption, it would be important to use state-of-the-art methodologies in real life applications. The contribution of the presented article to this matter is threefold. First, to facilitate the understanding of existing models', we developed a model evaluation method based on the attributes of industrial energy systems. We applied it to the reviewed scientific energy system models, providing the reader an overview of considered modelling approaches, design process steps, energy types, technological, economic and ecological aspects. Second, we conclude based on the results that current models for industrial cases are not completely suitable for wide practical implementation, among others because of the way they evaluate system operation and they do not incorporate every discussed requirement aspect. Third, this article draws up a potential modelling approach to fill these gaps, considering the missing points and omissions.

Suggested Citation

  • Urban, Kristof L. & Scheller, Fabian & Bruckner, Thomas, 2021. "Suitability assessment of models in the industrial energy system design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    • Handle: RePEc:eee:rensus:v:137:y:2021:i:c:s1364032120306882
    DOI: 10.1016/j.rser.2020.110400
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032120306882
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2020.110400?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. Ashouri, Araz & Fux, Samuel S. & Benz, Michael J. & Guzzella, Lino, 2013. "Optimal design and operation of building services using mixed-integer linear programming techniques," Energy, Elsevier, vol. 59(C), pages 365-376.
    2. Yang, Yun & Zhang, Shijie & Xiao, Yunhan, 2015. "Optimal design of distributed energy resource systems coupled with energy distribution networks," Energy, Elsevier, vol. 85(C), pages 433-448.
    3. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    4. Katti, Pradeep K. & Khedkar, Mohan K., 2007. "Alternative energy facilities based on site matching and generation unit sizing for remote area power supply," Renewable Energy, Elsevier, vol. 32(8), pages 1346-1362.
    5. Wolisz, Henryk & Schütz, Thomas & Blanke, Tobias & Hagenkamp, Markus & Kohrn, Markus & Wesseling, Mark & Müller, Dirk, 2017. "Cost optimal sizing of smart buildings' energy system components considering changing end-consumer electricity markets," Energy, Elsevier, vol. 137(C), pages 715-728.
    6. Erdinc, O. & Uzunoglu, M., 2012. "Optimum design of hybrid renewable energy systems: Overview of different approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(3), pages 1412-1425.
    7. Atabay, Dennis, 2017. "An open-source model for optimal design and operation of industrial energy systems," Energy, Elsevier, vol. 121(C), pages 803-821.
    8. Scott, James A. & Ho, William & Dey, Prasanta K., 2012. "A review of multi-criteria decision-making methods for bioenergy systems," Energy, Elsevier, vol. 42(1), pages 146-156.
    9. Acha, Salvador & Mariaud, Arthur & Shah, Nilay & Markides, Christos N., 2018. "Optimal design and operation of distributed low-carbon energy technologies in commercial buildings," Energy, Elsevier, vol. 142(C), pages 578-591.
    10. Haidar, Ahmed M.A. & John, Priscilla N. & Shawal, Mohd, 2011. "Optimal configuration assessment of renewable energy in Malaysia," Renewable Energy, Elsevier, vol. 36(2), pages 881-888.
    11. Zhou, Zhe & Zhang, Jianyun & Liu, Pei & Li, Zheng & Georgiadis, Michael C. & Pistikopoulos, Efstratios N., 2013. "A two-stage stochastic programming model for the optimal design of distributed energy systems," Applied Energy, Elsevier, vol. 103(C), pages 135-144.
    12. Fazlollahi, Samira & Becker, Gwenaelle & Ashouri, Araz & Maréchal, François, 2015. "Multi-objective, multi-period optimization of district energy systems: IV – A case study," Energy, Elsevier, vol. 84(C), pages 365-381.
    13. Yokoyama, Ryohei & Tokunaga, Akira & Wakui, Tetsuya, 2018. "Robust optimal design of energy supply systems under uncertain energy demands based on a mixed-integer linear model," Energy, Elsevier, vol. 153(C), pages 159-169.
    14. Falke, Tobias & Krengel, Stefan & Meinerzhagen, Ann-Kathrin & Schnettler, Armin, 2016. "Multi-objective optimization and simulation model for the design of distributed energy systems," Applied Energy, Elsevier, vol. 184(C), pages 1508-1516.
    15. Thiem, Sebastian & Danov, Vladimir & Metzger, Michael & Schäfer, Jochen & Hamacher, Thomas, 2017. "Project-level multi-modal energy system design - Novel approach for considering detailed component models and example case study for airports," Energy, Elsevier, vol. 133(C), pages 691-709.
    16. Worrell, Ernst & Laitner, John A & Ruth, Michael & Finman, Hodayah, 2003. "Productivity benefits of industrial energy efficiency measures," Energy, Elsevier, vol. 28(11), pages 1081-1098.
    17. Fleten, S.-E. & Maribu, K.M. & Wangensteen, I., 2007. "Optimal investment strategies in decentralized renewable power generation under uncertainty," Energy, Elsevier, vol. 32(5), pages 803-815.
    18. Baños, R. & Manzano-Agugliaro, F. & Montoya, F.G. & Gil, C. & Alcayde, A. & Gómez, J., 2011. "Optimization methods applied to renewable and sustainable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 1753-1766, May.
    19. Arcuri, P. & Florio, G. & Fragiacomo, P., 2007. "A mixed integer programming model for optimal design of trigeneration in a hospital complex," Energy, Elsevier, vol. 32(8), pages 1430-1447.
    20. Buoro, D. & Casisi, M. & De Nardi, A. & Pinamonti, P. & Reini, M., 2013. "Multicriteria optimization of a distributed energy supply system for an industrial area," Energy, Elsevier, vol. 58(C), pages 128-137.
    21. 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.
    22. Bracco, Stefano & Dentici, Gabriele & Siri, Silvia, 2013. "Economic and environmental optimization model for the design and the operation of a combined heat and power distributed generation system in an urban area," Energy, Elsevier, vol. 55(C), pages 1014-1024.
    23. Weber, C. & Shah, N., 2011. "Optimisation based design of a district energy system for an eco-town in the United Kingdom," Energy, Elsevier, vol. 36(2), pages 1292-1308.
    24. Li, Longxi & Mu, Hailin & Li, Nan & Li, Miao, 2016. "Economic and environmental optimization for distributed energy resource systems coupled with district energy networks," Energy, Elsevier, vol. 109(C), pages 947-960.
    25. Voll, Philip & Klaffke, Carsten & Hennen, Maike & Bardow, André, 2013. "Automated superstructure-based synthesis and optimization of distributed energy supply systems," Energy, Elsevier, vol. 50(C), pages 374-388.
    26. Connolly, D. & Lund, H. & Mathiesen, B.V. & Leahy, M., 2010. "A review of computer tools for analysing the integration of renewable energy into various energy systems," Applied Energy, Elsevier, vol. 87(4), pages 1059-1082, April.
    27. Li, Miao & Mu, Hailin & Li, Nan & Ma, Baoyu, 2016. "Optimal design and operation strategy for integrated evaluation of CCHP (combined cooling heating and power) system," Energy, Elsevier, vol. 99(C), pages 202-220.
    28. 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.
    29. Voll, Philip & Jennings, Mark & Hennen, Maike & Shah, Nilay & Bardow, André, 2015. "The optimum is not enough: A near-optimal solution paradigm for energy systems synthesis," Energy, Elsevier, vol. 82(C), pages 446-456.
    30. Evins, Ralph, 2013. "A review of computational optimisation methods applied to sustainable building design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 230-245.
    31. Rezvan, A. Taghipour & Gharneh, N. Shams & Gharehpetian, G.B., 2012. "Robust optimization of distributed generation investment in buildings," Energy, Elsevier, vol. 48(1), pages 455-463.
    32. Mills, Evan & Rosenfeld, Art, 1996. "Consumer non-energy benefits as a motivation for making energy-efficiency improvements," Energy, Elsevier, vol. 21(7), pages 707-720.
    33. Yu-Jun Zheng & Sheng-Yong Chen & Yao Lin & Wan-Liang Wang, 2013. "Bio-Inspired Optimization of Sustainable Energy Systems: A Review," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-12, January.
    34. Mehleri, Eugenia D. & Sarimveis, Haralambos & Markatos, Nikolaos C. & Papageorgiou, Lazaros G., 2012. "A mathematical programming approach for optimal design of distributed energy systems at the neighbourhood level," Energy, Elsevier, vol. 44(1), pages 96-104.
    35. Voll, Philip & Lampe, Matthias & Wrobel, Gregor & Bardow, André, 2012. "Superstructure-free synthesis and optimization of distributed industrial energy supply systems," Energy, Elsevier, vol. 45(1), pages 424-435.
    36. Mavrotas, George & Diakoulaki, Danae & Florios, Kostas & Georgiou, Paraskevas, 2008. "A mathematical programming framework for energy planning in services' sector buildings under uncertainty in load demand: The case of a hospital in Athens," Energy Policy, Elsevier, vol. 36(7), pages 2415-2429, July.
    37. Abbes, Dhaker & Martinez, André & Champenois, Gérard, 2014. "Life cycle cost, embodied energy and loss of power supply probability for the optimal design of hybrid power systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 98(C), pages 46-62.
    38. Alfonso González-Briones & Fernando De La Prieta & Mohd Saberi Mohamad & Sigeru Omatu & Juan M. Corchado, 2018. "Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review," Energies, MDPI, vol. 11(8), pages 1-28, July.
    39. Kavvadias, K.C. & Maroulis, Z.B., 2010. "Multi-objective optimization of a trigeneration plant," Energy Policy, Elsevier, vol. 38(2), pages 945-954, February.
    40. Ommen, Torben & Markussen, Wiebke Brix & Elmegaard, Brian, 2014. "Comparison of linear, mixed integer and non-linear programming methods in energy system dispatch modelling," Energy, Elsevier, vol. 74(C), pages 109-118.
    41. Fadaee, M. & Radzi, M.A.M., 2012. "Multi-objective optimization of a stand-alone hybrid renewable energy system by using evolutionary algorithms: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 3364-3369.
    42. Majewski, Dinah Elena & Lampe, Matthias & Voll, Philip & Bardow, André, 2017. "TRusT: A Two-stage Robustness Trade-off approach for the design of decentralized energy supply systems," Energy, Elsevier, vol. 118(C), pages 590-599.
    43. 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.
    44. Akbari, Kaveh & Jolai, Fariborz & Ghaderi, Seyed Farid, 2016. "Optimal design of distributed energy system in a neighborhood under uncertainty," Energy, Elsevier, vol. 116(P1), pages 567-582.
    45. DeCarolis, Joseph & Daly, Hannah & Dodds, Paul & Keppo, Ilkka & Li, Francis & McDowall, Will & Pye, Steve & Strachan, Neil & Trutnevyte, Evelina & Usher, Will & Winning, Matthew & Yeh, Sonia & Zeyring, 2017. "Formalizing best practice for energy system optimization modelling," Applied Energy, Elsevier, vol. 194(C), pages 184-198.
    46. Bajpai, Prabodh & Dash, Vaishalee, 2012. "Hybrid renewable energy systems for power generation in stand-alone applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 2926-2939.
    47. Zhang, Di & Evangelisti, Sara & Lettieri, Paola & Papageorgiou, Lazaros G., 2015. "Optimal design of CHP-based microgrids: Multiobjective optimisation and life cycle assessment," Energy, Elsevier, vol. 85(C), pages 181-193.
    48. Hafez, Omar & Bhattacharya, Kankar, 2012. "Optimal planning and design of a renewable energy based supply system for microgrids," Renewable Energy, Elsevier, vol. 45(C), pages 7-15.
    49. Ringkjøb, Hans-Kristian & Haugan, Peter M. & Solbrekke, Ida Marie, 2018. "A review of modelling tools for energy and electricity systems with large shares of variable renewables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 440-459.
    50. Sinha, Sunanda & Chandel, S.S., 2014. "Review of software tools for hybrid renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 192-205.
    51. Wu, Qiong & Ren, Hongbo & Gao, Weijun & Ren, Jianxing, 2016. "Multi-objective optimization of a distributed energy network integrated with heating interchange," Energy, Elsevier, vol. 109(C), pages 353-364.
    52. Ren, Hongbo & Zhou, Weisheng & Nakagami, Ken'ichi & Gao, Weijun, 2010. "Integrated design and evaluation of biomass energy system taking into consideration demand side characteristics," Energy, Elsevier, vol. 35(5), pages 2210-2222.
    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. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
    2. Karmellos, M. & Georgiou, P.N. & Mavrotas, G., 2019. "A comparison of methods for the optimal design of Distributed Energy Systems under uncertainty," Energy, Elsevier, vol. 178(C), pages 318-333.
    3. Wakui, Tetsuya & Hashiguchi, Moe & Sawada, Kento & Yokoyama, Ryohei, 2019. "Two-stage design optimization based on artificial immune system and mixed-integer linear programming for energy supply networks," Energy, Elsevier, vol. 170(C), pages 1228-1248.
    4. Mallikarjun, Sreekanth & Lewis, Herbert F., 2014. "Energy technology allocation for distributed energy resources: A strategic technology-policy framework," Energy, Elsevier, vol. 72(C), pages 783-799.
    5. 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.
    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. Mancarella, Pierluigi, 2014. "MES (multi-energy systems): An overview of concepts and evaluation models," Energy, Elsevier, vol. 65(C), pages 1-17.
    8. Chauhan, Anurag & Saini, R.P., 2014. "A review on Integrated Renewable Energy System based power generation for stand-alone applications: Configurations, storage options, sizing methodologies and control," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 99-120.
    9. Zhigang Duan & Yamin Yan & Xiaohan Yan & Qi Liao & Wan Zhang & Yongtu Liang & Tianqi Xia, 2017. "An MILP Method for Design of Distributed Energy Resource System Considering Stochastic Energy Supply and Demand," Energies, MDPI, vol. 11(1), pages 1-23, December.
    10. Fathima, A. Hina & Palanisamy, K., 2015. "Optimization in microgrids with hybrid energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 431-446.
    11. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    12. Alberto Fichera & Mattia Frasca & Rosaria Volpe, 2020. "A cost-based approach for evaluating the impact of a network of distributed energy systems on the centralized energy supply," Energy & Environment, , vol. 31(1), pages 77-87, February.
    13. Afzali, Sayyed Faridoddin & Cotton, James S. & Mahalec, Vladimir, 2020. "Urban community energy systems design under uncertainty for specified levels of carbon dioxide emissions," Applied Energy, Elsevier, vol. 259(C).
    14. Wakui, Tetsuya & Hashiguchi, Moe & Yokoyama, Ryohei, 2021. "Structural design of distributed energy networks by a hierarchical combination of variable- and constraint-based decomposition methods," Energy, Elsevier, vol. 224(C).
    15. Wakui, Tetsuya & Yokoyama, Ryohei, 2014. "Optimal structural design of residential cogeneration systems in consideration of their operating restrictions," Energy, Elsevier, vol. 64(C), pages 719-733.
    16. Wu, Qiong & Ren, Hongbo & Gao, Weijun & Ren, Jianxing, 2016. "Multi-objective optimization of a distributed energy network integrated with heating interchange," Energy, Elsevier, vol. 109(C), pages 353-364.
    17. Di Somma, M. & Yan, B. & Bianco, N. & Graditi, G. & Luh, P.B. & Mongibello, L. & Naso, V., 2017. "Multi-objective design optimization of distributed energy systems through cost and exergy assessments," Applied Energy, Elsevier, vol. 204(C), pages 1299-1316.
    18. Li, Longxi & Mu, Hailin & Li, Nan & Li, Miao, 2016. "Economic and environmental optimization for distributed energy resource systems coupled with district energy networks," Energy, Elsevier, vol. 109(C), pages 947-960.
    19. Ren, Fukang & Wei, Ziqing & Zhai, Xiaoqiang, 2022. "A review on the integration and optimization of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    20. Bracco, Stefano & Dentici, Gabriele & Siri, Silvia, 2016. "DESOD: a mathematical programming tool to optimally design a distributed energy system," Energy, Elsevier, vol. 100(C), pages 298-309.

    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:rensus:v:137:y:2021:i:c:s1364032120306882. 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/600126/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.