IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v247y2019icp755-765.html
   My bibliography  Save this article

Design of low-carbon utility systems: Exploiting time-dependent grid emissions for climate-friendly demand-side management

Author

Listed:
  • Baumgärtner, Nils
  • Delorme, Roman
  • Hennen, Maike
  • Bardow, André

Abstract

Efficient energy supply is key to reduce industrial greenhouse gas emissions. In the industry, energy is often supplied by on-site utility systems with electricity grid connection. Usually, electricity from the grid is assumed to have annually averaged emission factors when greenhouse gas emissions are calculated. However, emissions from electricity production are in fact time-dependent to match continuously varying demand and supply. These time dependent emissions offer the potential to reduce emissions by temporal shifting of electricity demand in demand-side management.

Suggested Citation

  • Baumgärtner, Nils & Delorme, Roman & Hennen, Maike & Bardow, André, 2019. "Design of low-carbon utility systems: Exploiting time-dependent grid emissions for climate-friendly demand-side management," Applied Energy, Elsevier, vol. 247(C), pages 755-765.
    • Handle: RePEc:eee:appene:v:247:y:2019:i:c:p:755-765
    DOI: 10.1016/j.apenergy.2019.04.029
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919306609
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.04.029?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. Mancarella, Pierluigi, 2014. "MES (multi-energy systems): An overview of concepts and evaluation models," Energy, Elsevier, vol. 65(C), pages 1-17.
    2. Alarcon-Rodriguez, Arturo & Ault, Graham & Galloway, Stuart, 2010. "Multi-objective planning of distributed energy resources: A review of the state-of-the-art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(5), pages 1353-1366, June.
    3. Finenko, Anton & Cheah, Lynette, 2016. "Temporal CO2 emissions associated with electricity generation: Case study of Singapore," Energy Policy, Elsevier, vol. 93(C), pages 70-79.
    4. Gabrel, Virginie & Murat, Cécile & Thiele, Aurélie, 2014. "Recent advances in robust optimization: An overview," European Journal of Operational Research, Elsevier, vol. 235(3), pages 471-483.
    5. Graff Zivin, Joshua S. & Kotchen, Matthew J. & Mansur, Erin T., 2014. "Spatial and temporal heterogeneity of marginal emissions: Implications for electric cars and other electricity-shifting policies," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PA), pages 248-268.
    6. Psomopoulos, C.S. & Skoula, I. & Karras, C. & Chatzimpiros, A. & Chionidis, M., 2010. "Electricity savings and CO2 emissions reduction in buildings sector: How important the network losses are in the calculation?," Energy, Elsevier, vol. 35(1), pages 485-490.
    7. 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.
    8. Bettle, R. & Pout, C.H. & Hitchin, E.R., 2006. "Interactions between electricity-saving measures and carbon emissions from power generation in England and Wales," Energy Policy, Elsevier, vol. 34(18), pages 3434-3446, December.
    9. Voorspools, Kris R. & D'haeseleer, William D., 2000. "An evaluation method for calculating the emission responsibility of specific electric applications," Energy Policy, Elsevier, vol. 28(13), pages 967-980, November.
    10. 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.
    11. Gamarra, Carlos & Guerrero, Josep M., 2015. "Computational optimization techniques applied to microgrids planning: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 413-424.
    12. Hawkes, A.D., 2010. "Estimating marginal CO2 emissions rates for national electricity systems," Energy Policy, Elsevier, vol. 38(10), pages 5977-5987, October.
    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. Shukai Liu & Liang Dong & Ling Han & Jiajia Huan & Baihao Qiao, 2022. "Efficiency versus System Synergism: An Advanced Life Cycle Assessment for a Novel Decarbonized Grid System Innovation," Energies, MDPI, vol. 15(12), pages 1-15, June.
    2. Biéron, M. & Le Dréau, J. & Haas, B., 2023. "Assessment of the marginal technologies reacting to demand response events: A French case-study," Energy, Elsevier, vol. 275(C).
    3. Navid Rezaei & Abdollah Ahmadi & Mohammadhossein Deihimi, 2022. "A Comprehensive Review of Demand-Side Management Based on Analysis of Productivity: Techniques and Applications," Energies, MDPI, vol. 15(20), pages 1-28, October.
    4. Mavromatidis, Georgios & Petkov, Ivalin, 2021. "MANGO: A novel optimization model for the long-term, multi-stage planning of decentralized multi-energy systems," Applied Energy, Elsevier, vol. 288(C).
    5. Wolf, Isabel & Holzapfel, Peter K.R. & Meschede, Henning & Finkbeiner, Matthias, 2023. "On the potential of temporally resolved GHG emission factors for load shifting: A case study on electrified steam generation," Applied Energy, Elsevier, vol. 348(C).
    6. Markus Fleschutz & Markus Bohlayer & Marco Braun & Michael D. Murphy, 2022. "Demand Response Analysis Framework (DRAF): An Open-Source Multi-Objective Decision Support Tool for Decarbonizing Local Multi-Energy Systems," Sustainability, MDPI, vol. 14(13), pages 1-38, June.
    7. Heffron, Raphael & Körner, Marc-Fabian & Wagner, Jonathan & Weibelzahl, Martin & Fridgen, Gilbert, 2020. "Industrial demand-side flexibility: A key element of a just energy transition and industrial development," Applied Energy, Elsevier, vol. 269(C).
    8. Markus Fleschutz & Markus Bohlayer & Marco Braun & Michael D. Murphy, 2023. "From prosumer to flexumer: Case study on the value of flexibility in decarbonizing the multi-energy system of a manufacturing company," Papers 2301.07997, arXiv.org.
    9. 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.
    10. 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).
    11. Fleschutz, Markus & Bohlayer, Markus & Braun, Marco & Henze, Gregor & Murphy, Michael D., 2021. "The effect of price-based demand response on carbon emissions in European electricity markets: The importance of adequate carbon prices," Applied Energy, Elsevier, vol. 295(C).
    12. Golmohamadi, Hessam, 2022. "Demand-side management in industrial sector: A review of heavy industries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).

    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. Hawkes, A.D., 2014. "Long-run marginal CO2 emissions factors in national electricity systems," Applied Energy, Elsevier, vol. 125(C), pages 197-205.
    2. Filippo Beltrami & Fulvio Fontini & Monica Giulietti & Luigi Grossi, 2022. "The Zonal and Seasonal CO2 Marginal Emissions Factors for the Italian Power Market," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 83(2), pages 381-411, October.
    3. Guanglei Wang & Hassan Hijazi, 2018. "Mathematical programming methods for microgrid design and operations: a survey on deterministic and stochastic approaches," Computational Optimization and Applications, Springer, vol. 71(2), pages 553-608, November.
    4. Howard, B. & Waite, M. & Modi, V., 2017. "Current and near-term GHG emissions factors from electricity production for New York State and New York City," Applied Energy, Elsevier, vol. 187(C), pages 255-271.
    5. Capuder, Tomislav & Mancarella, Pierluigi, 2014. "Techno-economic and environmental modelling and optimization of flexible distributed multi-generation options," Energy, Elsevier, vol. 71(C), pages 516-533.
    6. Syed Ali Abbas Kazmi & Muhammad Khuram Shahzad & Akif Zia Khan & Dong Ryeol Shin, 2017. "Smart Distribution Networks: A Review of Modern Distribution Concepts from a Planning Perspective," Energies, MDPI, vol. 10(4), pages 1-47, April.
    7. Scheller, Fabian & Bruckner, Thomas, 2019. "Energy system optimization at the municipal level: An analysis of modeling approaches and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 444-461.
    8. 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.
    9. Pimm, Andrew J. & Palczewski, Jan & Barbour, Edward R. & Cockerill, Tim T., 2021. "Using electricity storage to reduce greenhouse gas emissions," Applied Energy, Elsevier, vol. 282(PA).
    10. Rüdisüli, Martin & Romano, Elliot & Eggimann, Sven & Patel, Martin K., 2022. "Decarbonization strategies for Switzerland considering embedded greenhouse gas emissions in electricity imports," Energy Policy, Elsevier, vol. 162(C).
    11. 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.
    12. 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.
    13. Fuchs, Marcus & Teichmann, Jens & Lauster, Moritz & Remmen, Peter & Streblow, Rita & Müller, Dirk, 2016. "Workflow automation for combined modeling of buildings and district energy systems," Energy, Elsevier, vol. 117(P2), pages 478-484.
    14. 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.
    15. Fleschutz, Markus & Bohlayer, Markus & Braun, Marco & Henze, Gregor & Murphy, Michael D., 2021. "The effect of price-based demand response on carbon emissions in European electricity markets: The importance of adequate carbon prices," Applied Energy, Elsevier, vol. 295(C).
    16. Bigazzi, Alexander, 2019. "Comparison of marginal and average emission factors for passenger transportation modes," Applied Energy, Elsevier, vol. 242(C), pages 1460-1466.
    17. Aviso, Kathleen B. & Tan, Raymond R., 2018. "Fuzzy P-graph for optimal synthesis of cogeneration and trigeneration systems," Energy, Elsevier, vol. 154(C), pages 258-268.
    18. John Clauß & Sebastian Stinner & Christian Solli & Karen Byskov Lindberg & Henrik Madsen & Laurent Georges, 2019. "Evaluation Method for the Hourly Average CO 2eq. Intensity of the Electricity Mix and Its Application to the Demand Response of Residential Heating," Energies, MDPI, vol. 12(7), pages 1-25, April.
    19. Pinto, Edwin S. & Gronier, Timothé & Franquet, Erwin & Serra, Luis M., 2023. "Opportunities and economic assessment for a third-party delivering electricity, heat and cold to residential buildings," Energy, Elsevier, vol. 272(C).
    20. Li, Mo & Yang, Yi & Smith, Timothy M. & Wilson, Elizabeth J., 2020. "Wind can reduce storage-induced emissions at grid scales," Applied Energy, Elsevier, vol. 276(C).

    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:247:y:2019:i:c:p:755-765. 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.