IDEAS home Printed from https://ideas.repec.org/a/eee/enepol/v189y2024ics030142152400082x.html
   My bibliography  Save this article

How do dynamic electricity tariffs and different grid charge designs interact? - Implications for residential consumers and grid reinforcement requirements

Author

Listed:
  • Stute, Judith
  • Klobasa, Marian

Abstract

Dynamic electricity retail tariffs and different grid charge designs are discussed as key measures to support renewable energy integration. This article investigates the interplay between both, examining their impact on residential consumers regarding their economic savings and choice of retail tariff and on grid reinforcement requirements in low-voltage grids. We use a model-based approach for determining grid reinforcement requirements combined with an optimization model to assess residential consumer behavior towards different combinations of dynamic electricity retail tariffs and grid charge designs. We explore how these options influence the choice of households in Germany to invest in a home energy management system and to opt for a dynamic electricity retail tariff. Our findings show that with a grid charge design with capacity subscription, the share of households utilizing their flexibility and opting for a dynamic electricity retail tariff can be increased up to 74% (vs. 67% for a volumetric grid charge design). Furthermore, grid reinforcement costs can be reduced with a capacity subscription based grid charge design by 37% in rural low-voltage grids compared to the current grid charge design in Germany. This study offers novel perspectives on the interplay of dynamic electricity retail tariffs and grid charge designs, emphasizing the need for integrated policy approaches that allow residential consumers to benefit from reduced electricity costs while limiting grid reinforcement costs for distribution system operators.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:enepol:v:189:y:2024:i:c:s030142152400082x
    DOI: 10.1016/j.enpol.2024.114062
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030142152400082X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.enpol.2024.114062?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. Bergaentzle, Claire & Gunkel, Philipp Andreas, 2022. "Cross-sector flexibility, storage investment and the integration of renewables: Capturing the impacts of grid tariffs," Energy Policy, Elsevier, vol. 164(C).
    2. Steffen Meinecke & Džanan Sarajlić & Simon Ruben Drauz & Annika Klettke & Lars-Peter Lauven & Christian Rehtanz & Albert Moser & Martin Braun, 2020. "SimBench—A Benchmark Dataset of Electric Power Systems to Compare Innovative Solutions Based on Power Flow Analysis," Energies, MDPI, vol. 13(12), pages 1-19, June.
    3. Yunusov, Timur & Torriti, Jacopo, 2021. "Distributional effects of Time of Use tariffs based on electricity demand and time use," Energy Policy, Elsevier, vol. 156(C).
    4. Belton, Cameron A. & Lunn, Peter D., 2020. "Smart choices? An experimental study of smart meters and time-of-use tariffs in Ireland," Energy Policy, Elsevier, vol. 140(C).
    5. Lang, Corey & Qiu, Yueming (Lucy) & Dong, Luran, 2023. "Increasing voluntary enrollment in time-of-use electricity rates: Findings from a survey experiment," Energy Policy, Elsevier, vol. 173(C).
    6. Plötz, Patrick & Gnann, Till & Wietschel, Martin, 2014. "Modelling market diffusion of electric vehicles with real world driving data. Part I: Model structure and validation," Working Papers "Sustainability and Innovation" S4/2014, Fraunhofer Institute for Systems and Innovation Research (ISI).
    7. Bjarghov, Sigurd & Farahmand, Hossein & Doorman, Gerard, 2022. "Capacity subscription grid tariff efficiency and the impact of uncertainty on the subscribed level," Energy Policy, Elsevier, vol. 165(C).
    8. Avau, Michiel & Govaerts, Niels & Delarue, Erik, 2021. "Impact of distribution tariffs on prosumer demand response," Energy Policy, Elsevier, vol. 151(C).
    9. Hoarau, Quentin & Perez, Yannick, 2019. "Network tariff design with prosumers and electromobility: Who wins, who loses?," Energy Economics, Elsevier, vol. 83(C), pages 26-39.
    10. Nijhuis, M. & Gibescu, M. & Cobben, J.F.G., 2017. "Analysis of reflectivity & predictability of electricity network tariff structures for household consumers," Energy Policy, Elsevier, vol. 109(C), pages 631-641.
    11. von Loessl, Victor, 2023. "Smart meter-related data privacy concerns and dynamic electricity tariffs: Evidence from a stated choice experiment," Energy Policy, Elsevier, vol. 180(C).
    12. Buryk, Stephen & Mead, Doug & Mourato, Susana & Torriti, Jacopo, 2015. "Investigating preferences for dynamic electricity tariffs: The effect of environmental and system benefit disclosure," Energy Policy, Elsevier, vol. 80(C), pages 190-195.
    13. Khan, Ahsan Raza & Mahmood, Anzar & Safdar, Awais & Khan, Zafar A. & Khan, Naveed Ahmed, 2016. "Load forecasting, dynamic pricing and DSM in smart grid: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1311-1322.
    14. Plötz, Patrick & Gnann, Till & Wietschel, Martin, 2014. "Modelling market diffusion of electric vehicles with real world driving data — Part I: Model structure and validation," Ecological Economics, Elsevier, vol. 107(C), pages 411-421.
    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. Klingler, Anna-Lena, 2017. "Self-consumption with PV+Battery systems: A market diffusion model considering individual consumer behaviour and preferences," Applied Energy, Elsevier, vol. 205(C), pages 1560-1570.
    17. Yan, Xing & Ozturk, Yusuf & Hu, Zechun & Song, Yonghua, 2018. "A review on price-driven residential demand response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 411-419.
    18. Backe, Stian & Kara, Güray & Tomasgard, Asgeir, 2020. "Comparing individual and coordinated demand response with dynamic and static power grid tariffs," Energy, Elsevier, vol. 201(C).
    19. Pena-Bello, Alejandro & Junod, Robin & Ballif, Christophe & Wyrsch, Nicolas, 2023. "Balancing DSO interests and PV system economics with alternative tariffs," Energy Policy, Elsevier, vol. 183(C).
    20. Staffell, Iain & Pfenninger, Stefan, 2016. "Using bias-corrected reanalysis to simulate current and future wind power output," Energy, Elsevier, vol. 114(C), pages 1224-1239.
    21. Schleich, Joachim & Klobasa, Marian & Brunner, Marc & Gölz, Sebastian & Götz, Konrad, 2011. "Smart metering in Germany and Austria: Results of providing feedback information in a field trial," Working Papers "Sustainability and Innovation" S6/2011, Fraunhofer Institute for Systems and Innovation Research (ISI).
    22. Parrish, Bryony & Heptonstall, Phil & Gross, Rob & Sovacool, Benjamin K., 2020. "A systematic review of motivations, enablers and barriers for consumer engagement with residential demand response," Energy Policy, Elsevier, vol. 138(C).
    23. 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)

    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. 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).
    2. Kühnbach, Matthias & Bekk, Anke & Weidlich, Anke, 2022. "Towards improved prosumer participation: Electricity trading in local markets," Energy, Elsevier, vol. 239(PE).
    3. Javier Borquez & Hector Chavez & Karina A. Barbosa & Marcela Jamett & Rodrigo Acuna, 2020. "A Simple Distribution Energy Tariff under the Penetration of DG," Energies, MDPI, vol. 13(8), pages 1-17, April.
    4. Günther, Claudia & Schill, Wolf-Peter & Zerrahn, Alexander, 2021. "Prosumage of solar electricity: Tariff design, capacity investments, and power sector effects," Energy Policy, Elsevier, vol. 152(C).
    5. Vaughan, Jim & Doumen, Sjoerd C. & Kok, Koen, 2023. "Empowering tomorrow, controlling today: A multi-criteria assessment of distribution grid tariff designs," Applied Energy, Elsevier, vol. 341(C).
    6. Thomaßen, Georg & Redl, Christian & Bruckner, Thomas, 2022. "Will the energy-only market collapse? On market dynamics in low-carbon electricity systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 164(C).
    7. Askeland, Magnus & Backe, Stian & Bjarghov, Sigurd & Korpås, Magnus, 2021. "Helping end-users help each other: Coordinating development and operation of distributed resources through local power markets and grid tariffs," Energy Economics, Elsevier, vol. 94(C).
    8. Schwab, Julia & Sölch, Christian & Zöttl, Gregor, 2022. "Electric Vehicle Cost in 2035: The impact of market penetration and charging strategies," Energy Economics, Elsevier, vol. 114(C).
    9. Claudia Gunther & Wolf-Peter Schill & Alexander Zerrahn, 2019. "Prosumage of solar electricity: tariff design, capacity investments, and power system effects," Papers 1907.09855, arXiv.org.
    10. Morell-Dameto, Nicolás & Chaves-Ávila, José Pablo & Gómez San Román, Tomás & Schittekatte, Tim, 2023. "Forward-looking dynamic network charges for real-world electricity systems: A Slovenian case study," Energy Economics, Elsevier, vol. 125(C).
    11. Qiu, Yueming Lucy & Wang, Yi David & Iseki, Hiroyuki & Shen, Xingchi & Xing, Bo & Zhang, Huiming, 2022. "Empirical grid impact of in-home electric vehicle charging differs from predictions," Resource and Energy Economics, Elsevier, vol. 67(C).
    12. Olkkonen, Ville & Lind, Arne & Rosenberg, Eva & Kvalbein, Lisa, 2023. "Electrification of the agricultural sector in Norway in an effort to phase out fossil fuel consumption," Energy, Elsevier, vol. 276(C).
    13. Wong, Pui Ting & Rau, Henrike, 2023. "Time of Use tariffs, childcare and everyday temporalities in the US and China: Evidence from time-use and sequence-network analysis," Energy Policy, Elsevier, vol. 172(C).
    14. Marko Hočevar & Lovrenc Novak & Primož Drešar & Gašper Rak, 2022. "The Status Quo and Future of Hydropower in Slovenia," Energies, MDPI, vol. 15(19), pages 1-13, September.
    15. Gnann, Till & Stephens, Thomas S. & Lin, Zhenhong & Plötz, Patrick & Liu, Changzheng & Brokate, Jens, 2018. "What drives the market for plug-in electric vehicles? - A review of international PEV market diffusion models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 158-164.
    16. Van, Tien Linh Cao & Barthelmes, Lukas & Gnann, Till & Speth, Daniel & Kagerbauer, Martin, 2021. "Addressing the gaps in market diffusion modeling of electrical vehicles: A case study from Germany for the integration of environmental policy measures," Working Papers "Sustainability and Innovation" S05/2021, Fraunhofer Institute for Systems and Innovation Research (ISI).
    17. Lukas Kriechbaum & Philipp Gradl & Romeo Reichenhauser & Thomas Kienberger, 2020. "Modelling Grid Constraints in a Multi-Energy Municipal Energy System Using Cumulative Exergy Consumption Minimisation," Energies, MDPI, vol. 13(15), pages 1-23, July.
    18. Behrang Shirizadeh, Quentin Perrier, and Philippe Quirion, 2022. "How Sensitive are Optimal Fully Renewable Power Systems to Technology Cost Uncertainty?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    19. 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).
    20. Liu, Hailiang & Andresen, Gorm Bruun & Greiner, Martin, 2018. "Cost-optimal design of a simplified highly renewable Chinese electricity network," Energy, Elsevier, vol. 147(C), pages 534-546.

    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:enepol:v:189:y:2024:i:c:s030142152400082x. 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/locate/enpol .

    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.