IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i15p2945-d253493.html
   My bibliography  Save this article

Systematic Integration of Energy-Optimal Buildings With District Networks

Author

Listed:
  • Raluca Suciu

    (Industrial Process and Energy Systems Engineering (IPESE), École Polytechnique Fédérale de Lausanne, CH-1951 Sion, Switzerland)

  • Paul Stadler

    (Industrial Process and Energy Systems Engineering (IPESE), École Polytechnique Fédérale de Lausanne, CH-1951 Sion, Switzerland)

  • Ivan Kantor

    (Industrial Process and Energy Systems Engineering (IPESE), École Polytechnique Fédérale de Lausanne, CH-1951 Sion, Switzerland)

  • Luc Girardin

    (Industrial Process and Energy Systems Engineering (IPESE), École Polytechnique Fédérale de Lausanne, CH-1951 Sion, Switzerland)

  • François Maréchal

    (Industrial Process and Energy Systems Engineering (IPESE), École Polytechnique Fédérale de Lausanne, CH-1951 Sion, Switzerland)

Abstract

The residential sector accounts for a large share of worldwide energy consumption, yet is difficult to characterise, since consumption profiles depend on several factors from geographical location to individual building occupant behaviour. Given this difficulty, the fact that energy used in this sector is primarily derived from fossil fuels and the latest energy policies around the world (e.g., Europe 20-20-20), a method able to systematically integrate multi-energy networks and low carbon resources in urban systems is clearly required. This work proposes such a method, which uses process integration techniques and mixed integer linear programming to optimise energy systems at both the individual building and district levels. Parametric optimisation is applied as a systematic way to generate interesting solutions for all budgets (i.e., investment cost limits) and two approaches to temporal data treatment are evaluated: monthly average and hourly typical day resolution. The city center of Geneva is used as a first case study to compare the time resolutions and results highlight that implicit peak shaving occurs when data are reduced to monthly averages. Consequently, solutions reveal lower operating costs and higher self-sufficiency scenarios compared to using a finer resolution but with similar relative cost contributions. Therefore, monthly resolution is used for the second case study, the whole canton of Geneva, in the interest of reducing the data processing and computation time as a primary objective of the study is to discover the main cost contributors. The canton is used as a case study to analyse the penetration of low temperature, CO 2 -based, advanced fourth generation district energy networks with population density. The results reveal that only areas with a piping cost lower than 21.5 k€/100 m 2 ERA connect to the low-temperature network in the intermediate scenarios, while all areas must connect to achieve the minimum operating cost result. Parallel coordinates are employed to better visualise the key performance indicators at canton and commune level together with the breakdown of energy (electricity and natural gas) imports/exports and investment cost to highlight the main contributors.

Suggested Citation

  • Raluca Suciu & Paul Stadler & Ivan Kantor & Luc Girardin & François Maréchal, 2019. "Systematic Integration of Energy-Optimal Buildings With District Networks," Energies, MDPI, vol. 12(15), pages 1-38, July.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:15:p:2945-:d:253493
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/15/2945/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/12/15/2945/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Ligang & Pérez-Fortes, Mar & Madi, Hossein & Diethelm, Stefan & herle, Jan Van & Maréchal, François, 2018. "Optimal design of solid-oxide electrolyzer based power-to-methane systems: A comprehensive comparison between steam electrolysis and co-electrolysis," Applied Energy, Elsevier, vol. 211(C), pages 1060-1079.
    2. Mancarella, Pierluigi, 2014. "MES (multi-energy systems): An overview of concepts and evaluation models," Energy, Elsevier, vol. 65(C), pages 1-17.
    3. Glaeser, Edward L. & Kahn, Matthew E., 2010. "The greenness of cities: Carbon dioxide emissions and urban development," Journal of Urban Economics, Elsevier, vol. 67(3), pages 404-418, May.
    4. Girardin, Luc & Marechal, François & Dubuis, Matthias & Calame-Darbellay, Nicole & Favrat, Daniel, 2010. "EnerGis: A geographical information based system for the evaluation of integrated energy conversion systems in urban areas," Energy, Elsevier, vol. 35(2), pages 830-840.
    5. 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.
    6. 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.
    7. Henchoz, Samuel & Weber, Céline & Maréchal, François & Favrat, Daniel, 2015. "Performance and profitability perspectives of a CO2 based district energy network in Geneva's City Centre," Energy, Elsevier, vol. 85(C), pages 221-235.
    8. Molyneaux, A. & Leyland, G. & Favrat, D., 2010. "Environomic multi-objective optimisation of a district heating network considering centralized and decentralized heat pumps," Energy, Elsevier, vol. 35(2), pages 751-758.
    9. Keirstead, James & Jennings, Mark & Sivakumar, Aruna, 2012. "A review of urban energy system models: Approaches, challenges and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3847-3866.
    10. Suciu, Raluca & Girardin, Luc & Maréchal, François, 2018. "Energy integration of CO2 networks and power to gas for emerging energy autonomous cities in Europe," Energy, Elsevier, vol. 157(C), pages 830-842.
    11. Fazlollahi, Samira & Mandel, Pierre & Becker, Gwenaelle & Maréchal, Francois, 2012. "Methods for multi-objective investment and operating optimization of complex energy systems," Energy, Elsevier, vol. 45(1), pages 12-22.
    12. Niemi, R. & Mikkola, J. & Lund, P.D., 2012. "Urban energy systems with smart multi-carrier energy networks and renewable energy generation," Renewable Energy, Elsevier, vol. 48(C), pages 524-536.
    13. Luthander, Rasmus & Widén, Joakim & Nilsson, Daniel & Palm, Jenny, 2015. "Photovoltaic self-consumption in buildings: A review," Applied Energy, Elsevier, vol. 142(C), pages 80-94.
    14. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Marko Milojević & Paweł Nowodziński & Ivica Terzić & Svetlana Danshina, 2021. "Households’ Energy Autonomy: Risks or Benefits for a State?," Energies, MDPI, vol. 14(7), pages 1-16, April.
    2. Kachirayil, Febin & Weinand, Jann Michael & Scheller, Fabian & McKenna, Russell, 2022. "Reviewing local and integrated energy system models: insights into flexibility and robustness challenges," Applied Energy, Elsevier, vol. 324(C).
    3. Tan Yigitcanlar & Hoon Han & Md. Kamruzzaman, 2019. "Approaches, Advances, and Applications in the Sustainable Development of Smart Cities: A Commentary from the Guest Editors," Energies, MDPI, vol. 12(23), pages 1-11, November.

    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. Ferrari, Simone & Zagarella, Federica & Caputo, Paola & D'Amico, Antonino, 2019. "Results of a literature review on methods for estimating buildings energy demand at district level," Energy, Elsevier, vol. 175(C), pages 1130-1137.
    2. Allegrini, Jonas & Orehounig, Kristina & Mavromatidis, Georgios & Ruesch, Florian & Dorer, Viktor & Evins, Ralph, 2015. "A review of modelling approaches and tools for the simulation of district-scale energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1391-1404.
    3. 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).
    4. Waibel, Christoph & Evins, Ralph & Carmeliet, Jan, 2019. "Co-simulation and optimization of building geometry and multi-energy systems: Interdependencies in energy supply, energy demand and solar potentials," Applied Energy, Elsevier, vol. 242(C), pages 1661-1682.
    5. Menon, Ramanunni P. & Paolone, Mario & Maréchal, François, 2013. "Study of optimal design of polygeneration systems in optimal control strategies," Energy, Elsevier, vol. 55(C), pages 134-141.
    6. 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.
    7. 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.
    8. Mancarella, Pierluigi, 2014. "MES (multi-energy systems): An overview of concepts and evaluation models," Energy, Elsevier, vol. 65(C), pages 1-17.
    9. Miglani, Somil & Orehounig, Kristina & Carmeliet, Jan, 2018. "Integrating a thermal model of ground source heat pumps and solar regeneration within building energy system optimization," Applied Energy, Elsevier, vol. 218(C), pages 78-94.
    10. Song, Jeonghun & Oh, Si-Doek & Song, Seung Jin, 2019. "Effect of increased building-integrated renewable energy on building energy portfolio and energy flows in an urban district of Korea," Energy, Elsevier, vol. 189(C).
    11. Schütz, Thomas & Schraven, Markus Hans & Remy, Sebastian & Granacher, Julia & Kemetmüller, Dominik & Fuchs, Marcus & Müller, Dirk, 2017. "Optimal design of energy conversion units for residential buildings considering German market conditions," Energy, Elsevier, vol. 139(C), pages 895-915.
    12. Nils Korber & Maximilian Rohrig & Andreas Ulbig, 2022. "A stakeholder-oriented multi-criteria optimization model for decentral multi-energy systems," Papers 2204.06545, arXiv.org.
    13. Gabrielli, Paolo & Gazzani, Matteo & Mazzotti, Marco, 2018. "Electrochemical conversion technologies for optimal design of decentralized multi-energy systems: Modeling framework and technology assessment," Applied Energy, Elsevier, vol. 221(C), pages 557-575.
    14. David Drysdale & Brian Vad Mathiesen & Henrik Lund, 2019. "From Carbon Calculators to Energy System Analysis in Cities," Energies, MDPI, vol. 12(12), pages 1-21, June.
    15. Georgiou, Giorgos S. & Christodoulides, Paul & Kalogirou, Soteris A., 2019. "Real-time energy convex optimization, via electrical storage, in buildings – A review," Renewable Energy, Elsevier, vol. 139(C), pages 1355-1365.
    16. Alhamwi, Alaa & Medjroubi, Wided & Vogt, Thomas & Agert, Carsten, 2018. "Modelling urban energy requirements using open source data and models," Applied Energy, Elsevier, vol. 231(C), pages 1100-1108.
    17. Mastrucci, Alessio & Marvuglia, Antonino & Leopold, Ulrich & Benetto, Enrico, 2017. "Life Cycle Assessment of building stocks from urban to transnational scales: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 316-332.
    18. Silva, Mafalda C. & Horta, Isabel M. & Leal, Vítor & Oliveira, Vítor, 2017. "A spatially-explicit methodological framework based on neural networks to assess the effect of urban form on energy demand," Applied Energy, Elsevier, vol. 202(C), pages 386-398.
    19. Mittelviefhaus, Moritz & Pareschi, Giacomo & Allan, James & Georges, Gil & Boulouchos, Konstantinos, 2021. "Optimal investment and scheduling of residential multi-energy systems including electric mobility: A cost-effective approach to climate change mitigation," Applied Energy, Elsevier, vol. 301(C).
    20. Huang, Ying & Liao, Cuiping & Zhang, Jingjing & Guo, Hongxu & Zhou, Nan & Zhao, Daiqing, 2019. "Exploring potential pathways towards urban greenhouse gas peaks: A case study of Guangzhou, China," Applied Energy, Elsevier, vol. 251(C), pages 1-1.

    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:gam:jeners:v:12:y:2019:i:15:p:2945-:d:253493. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.