IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v343y2023ics0306261923005111.html
   My bibliography  Save this article

Optimization-based framework for low-voltage grid reinforcement assessment under various levels of flexibility and coordination

Author

Listed:
  • Candas, Soner
  • Reveron Baecker, Beneharo
  • Mohapatra, Anurag
  • Hamacher, Thomas

Abstract

The rapid electrification of residential heating and mobility sectors is expected to drive the existing distribution grid assets beyond their planned operating conditions This change will also reveal new potentials through sector coupling, flexibilities, and the local exchange of decentralized generation. This paper thus presents an optimization framework for multi-modal energy systems at the low voltage (LV) distribution grid level. In this, we focus on the reinforcement requirements of the grid and the techno-economic assessment of flexibility components and coordination between agents. By employing a multi-level approach, computational complexity is reduced, and various levels of coordination and flexibilities are implemented. We conclude the work with a case study for a representative rural grid in Germany, in which we observe high economic potential in the flexible operation of buildings, majorly thanks to better integration of photovoltaics. Across all paradigms barring a best-case benchmark, grid reinforcement based on a mix of passive and active measures was necessary. A synergy effect is observed between flexibilities and coordination, as their combination reduces the peaks to the extent of completely avoiding grid reinforcement. The presented framework can be applied with a wide range of grid and component types to outline the broad landscape of future LV distribution grids.

Suggested Citation

  • Candas, Soner & Reveron Baecker, Beneharo & Mohapatra, Anurag & Hamacher, Thomas, 2023. "Optimization-based framework for low-voltage grid reinforcement assessment under various levels of flexibility and coordination," Applied Energy, Elsevier, vol. 343(C).
    • Handle: RePEc:eee:appene:v:343:y:2023:i:c:s0306261923005111
    DOI: 10.1016/j.apenergy.2023.121147
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923005111
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.121147?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. Grimm, Veronika & Grübel, Julia & Rückel, Bastian & Sölch, Christian & Zöttl, Gregor, 2020. "Storage investment and network expansion in distribution networks: The impact of regulatory frameworks," Applied Energy, Elsevier, vol. 262(C).
    2. Love, Jenny & Smith, Andrew Z.P. & Watson, Stephen & Oikonomou, Eleni & Summerfield, Alex & Gleeson, Colin & Biddulph, Phillip & Chiu, Lai Fong & Wingfield, Jez & Martin, Chris & Stone, Andy & Lowe, R, 2017. "The addition of heat pump electricity load profiles to GB electricity demand: Evidence from a heat pump field trial," Applied Energy, Elsevier, vol. 204(C), pages 332-342.
    3. Timilsina, Govinda R., 2021. "Are renewable energy technologies cost competitive for electricity generation?," Renewable Energy, Elsevier, vol. 180(C), pages 658-672.
    4. Bernd Thormann & Thomas Kienberger, 2020. "Evaluation of Grid Capacities for Integrating Future E-Mobility and Heat Pumps into Low-Voltage Grids," Energies, MDPI, vol. 13(19), pages 1-30, September.
    5. Hoffmann, Maximilian & Kotzur, Leander & Stolten, Detlef, 2022. "The Pareto-optimal temporal aggregation of energy system models," Applied Energy, Elsevier, vol. 315(C).
    6. 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.
    7. Pio Baake & Sebastian Schwenen & Christian von Hirschhausen, 2020. "Local Power Markets," Discussion Papers of DIW Berlin 1904, DIW Berlin, German Institute for Economic Research.
    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. Sedghi, Mahdi & Ahmadian, Ali & Aliakbar-Golkar, Masoud, 2016. "Assessment of optimization algorithms capability in distribution network planning: Review, comparison and modification techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 415-434.
    10. 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.
    11. Steffen, Bjarne, 2020. "Estimating the cost of capital for renewable energy projects," Energy Economics, Elsevier, vol. 88(C).
    12. Reveron Baecker, Beneharo & Candas, Soner, 2022. "Co-optimizing transmission and active distribution grids to assess demand-side flexibilities of a carbon-neutral German energy system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    13. Spiliotis, Konstantinos & Ramos Gutierrez, Ariana Isabel & Belmans, Ronnie, 2016. "Demand flexibility versus physical network expansions in distribution grids," Applied Energy, Elsevier, vol. 182(C), pages 613-624.
    14. Carroll, P. & Chesser, M. & Lyons, P., 2020. "Air Source Heat Pumps field studies: A systematic literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    15. Pfenninger, Stefan & Staffell, Iain, 2016. "Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data," Energy, Elsevier, vol. 114(C), pages 1251-1265.
    16. Bayer, Benjamin & Matschoss, Patrick & Thomas, Heiko & Marian, Adela, 2018. "The German experience with integrating photovoltaic systems into the low-voltage grids," Renewable Energy, Elsevier, vol. 119(C), pages 129-141.
    17. Schittekatte, Tim & Momber, Ilan & Meeus, Leonardo, 2018. "Future-proof tariff design: Recovering sunk grid costs in a world where consumers are pushing back," Energy Economics, Elsevier, vol. 70(C), pages 484-498.
    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. Zinsmeister, Daniel & Tzscheutschler, Peter & Perić, Vedran S. & Goebel, Christoph, 2023. "Stratified thermal energy storage model with constant layer volume for predictive control — Formulation, comparison, and empirical validation," Renewable Energy, Elsevier, vol. 219(P2).
    2. Zare Oskouei, Morteza & Gharehpetian, Gevork B., 2024. "Flexibility enhancement of multi-district DISCOs considering a trade-off between congestion and extractable reserve capacity from virtual energy storage systems," Applied Energy, Elsevier, vol. 353(PB).
    3. Markus Doepfert & Soner Candas & Hermann Kraus & Peter Tzscheutschler & Thomas Hamacher, 2024. "Assessing the techno-economic benefits of LEMs for different grid topologies and prosumer shares," Papers 2410.13330, arXiv.org.

    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. Heendeniya, Charitha Buddhika & Sumper, Andreas & Eicker, Ursula, 2020. "The multi-energy system co-planning of nearly zero-energy districts – Status-quo and future research potential," Applied Energy, Elsevier, vol. 267(C).
    2. Timo Kannengießer & Maximilian Hoffmann & Leander Kotzur & Peter Stenzel & Fabian Schuetz & Klaus Peters & Stefan Nykamp & Detlef Stolten & Martin Robinius, 2019. "Reducing Computational Load for Mixed Integer Linear Programming: An Example for a District and an Island Energy System," Energies, MDPI, vol. 12(14), pages 1-27, July.
    3. De Mel, Ishanki & Klymenko, Oleksiy V. & Short, Michael, 2024. "Discrete optimal designs for distributed energy systems with nonconvex multiphase optimal power flow," Applied Energy, Elsevier, vol. 353(PB).
    4. Holjevac, Ninoslav & Capuder, Tomislav & Zhang, Ning & Kuzle, Igor & Kang, Chongqing, 2017. "Corrective receding horizon scheduling of flexible distributed multi-energy microgrids," Applied Energy, Elsevier, vol. 207(C), pages 176-194.
    5. Manfren, Massimiliano & Nastasi, Benedetto & Groppi, Daniele & Astiaso Garcia, Davide, 2020. "Open data and energy analytics - An analysis of essential information for energy system planning, design and operation," Energy, Elsevier, vol. 213(C).
    6. Peacock, Malcolm & Fragaki, Aikaterini & Matuszewski, Bogdan J, 2023. "The impact of heat electrification on the seasonal and interannual electricity demand of Great Britain," Applied Energy, Elsevier, vol. 337(C).
    7. Omoyele, Olalekan & Hoffmann, Maximilian & Koivisto, Matti & Larrañeta, Miguel & Weinand, Jann Michael & Linßen, Jochen & Stolten, Detlef, 2024. "Increasing the resolution of solar and wind time series for energy system modeling: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    8. 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.
    9. Vidas, Leonardo & Castro, Rui & Bosisio, Alessandro & Pires, Armando, 2024. "Optimal sizing of renewables-to-hydrogen systems in a suitable-site-selection geospatial framework: The case study of Italy and Portugal," Renewable and Sustainable Energy Reviews, Elsevier, vol. 202(C).
    10. Petrelli, Marina & Fioriti, Davide & Berizzi, Alberto & Bovo, Cristian & Poli, Davide, 2021. "A novel multi-objective method with online Pareto pruning for multi-year optimization of rural microgrids," Applied Energy, Elsevier, vol. 299(C).
    11. Finke, Jonas & Bertsch, Valentin, 2022. "Implementing a highly adaptable method for the multi-objective optimisation of energy systems," MPRA Paper 115504, University Library of Munich, Germany.
    12. Stute, Judith & Klobasa, Marian, 2024. "How do dynamic electricity tariffs and different grid charge designs interact? - Implications for residential consumers and grid reinforcement requirements," Energy Policy, Elsevier, vol. 189(C).
    13. Marquant, Julien F. & Evins, Ralph & Bollinger, L. Andrew & Carmeliet, Jan, 2017. "A holarchic approach for multi-scale distributed energy system optimisation," Applied Energy, Elsevier, vol. 208(C), pages 935-953.
    14. Semmelmann, Leo & Hertel, Matthias & Kircher, Kevin J. & Mikut, Ralf & Hagenmeyer, Veit & Weinhardt, Christof, 2024. "The impact of heat pumps on day-ahead energy community load forecasting," Applied Energy, Elsevier, vol. 368(C).
    15. Günther, Claudia & Schill, Wolf-Peter & Zerrahn, Alexander, 2021. "Prosumage of solar electricity: Tariff design, capacity investments, and power sector effects," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 152.
    16. Salvatore Favuzza & Mariano Giuseppe Ippolito & Fabio Massaro & Rossano Musca & Eleonora Riva Sanseverino & Giuseppe Schillaci & Gaetano Zizzo, 2018. "Building Automation and Control Systems and Electrical Distribution Grids: A Study on the Effects of Loads Control Logics on Power Losses and Peaks," Energies, MDPI, vol. 11(3), pages 1-15, March.
    17. Backe, Stian & Zwickl-Bernhard, Sebastian & Schwabeneder, Daniel & Auer, Hans & Korpås, Magnus & Tomasgard, Asgeir, 2022. "Impact of energy communities on the European electricity and heating system decarbonization pathway: Comparing local and global flexibility responses," Applied Energy, Elsevier, vol. 323(C).
    18. Yang, Dongfeng & Jiang, Chao & Cai, Guowei & Yang, Deyou & Liu, Xiaojun, 2020. "Interval method based optimal planning of multi-energy microgrid with uncertain renewable generation and demand," Applied Energy, Elsevier, vol. 277(C).
    19. Finke, Jonas & Bertsch, Valentin, 2023. "Implementing a highly adaptable method for the multi-objective optimisation of energy systems," Applied Energy, Elsevier, vol. 332(C).
    20. Noussan, Michel & Jarre, Matteo & Roberto, Roberta & Russolillo, Daniele, 2018. "Combined vs separate heat and power production – Primary energy comparison in high renewable share contexts," Applied Energy, Elsevier, vol. 213(C), pages 1-10.

    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:343:y:2023:i:c:s0306261923005111. 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.