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Efficient Buyer Groups With Prediction-of-Use Electricity Tariffs

Author

Listed:
  • Valentin Robu

    (HWU - Heriot-Watt University [Edinburgh])

  • Meritxell Vinyals

    (LADIS (CEA, LIST) - Laboratoire d'analyse des données et d'intelligence des systèmes (CEA, LIST) - DM2I (CEA, LIST) - Département Métrologie Instrumentation & Information (CEA, LIST) - LIST (CEA) - Laboratoire d'Intégration des Systèmes et des Technologies - DRT (CEA) - Direction de Recherche Technologique (CEA) - CEA - Commissariat à l'énergie atomique et aux énergies alternatives - Université Paris-Saclay)

  • Alex Rogers

    (University of Southampton)

  • Nicholas B. Jennings

    (Imperial College London)

Abstract

Current electricity tariffs do not reflect the real costs that a customer incurs to a supplier, as units are charged at the same rate, regardless of the consumption pattern. In this paper, we propose a prediction-of-use (POU) tariff that better reflects the predictability cost of a customer. Our tariff asks customers to pre-commit to a baseline consumption, and charges them based on both their actual consumption and the deviation from the anticipated baseline. First, we study, from a cooperative game theory perspective, the cost game induced by a single such tariff, and show customers would have an incentive to minimize their risk, by joining together when buying electricity as a grand coalition. Second, we study the efficient (i.e., cost-minimizing) structure of buying groups for the more realistic setting when multiple, competing POU tariffs are available. We propose a polynomial time algorithm to compute the efficient buyer groups, and validate our approach experimentally, using a large-scale data set of domestic consumers in the U.K.

Suggested Citation

  • Valentin Robu & Meritxell Vinyals & Alex Rogers & Nicholas B. Jennings, 2018. "Efficient Buyer Groups With Prediction-of-Use Electricity Tariffs," Post-Print cea-01917811, HAL.
  • Handle: RePEc:hal:journl:cea-01917811
    DOI: 10.1109/TSG.2017.2660580
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    Citations

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    Cited by:

    1. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    2. Norbu, Sonam & Couraud, Benoit & Robu, Valentin & Andoni, Merlinda & Flynn, David, 2021. "Modelling the redistribution of benefits from joint investments in community energy projects," Applied Energy, Elsevier, vol. 287(C).
    3. Khan, Hafiz Anwar Ullah & Ünel, Burçin & Dvorkin, Yury, 2023. "Electricity Tariff Design via Lens of Energy Justice," Omega, Elsevier, vol. 117(C).
    4. Kirli, Desen & Couraud, Benoit & Robu, Valentin & Salgado-Bravo, Marcelo & Norbu, Sonam & Andoni, Merlinda & Antonopoulos, Ioannis & Negrete-Pincetic, Matias & Flynn, David & Kiprakis, Aristides, 2022. "Smart contracts in energy systems: A systematic review of fundamental approaches and implementations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    5. Zhang, Ying & Robu, Valentin & Cremers, Sho & Norbu, Sonam & Couraud, Benoit & Andoni, Merlinda & Flynn, David & Poor, H. Vincent, 2024. "Modelling the formation of peer-to-peer trading coalitions and prosumer participation incentives in transactive energy communities," Applied Energy, Elsevier, vol. 355(C).
    6. Cremers, Sho & Robu, Valentin & Zhang, Peter & Andoni, Merlinda & Norbu, Sonam & Flynn, David, 2023. "Efficient methods for approximating the Shapley value for asset sharing in energy communities," Applied Energy, Elsevier, vol. 331(C).
    7. Gustavo E. Coria & Angel M. Sanchez & Ameena S. Al-Sumaiti & Guiseppe A. Rattá & Sergio R. Rivera & Andrés A. Romero, 2019. "A Framework for Determining a Prediction-Of-Use Tariff Aimed at Coordinating Aggregators of Plug-In Electric Vehicles," Energies, MDPI, vol. 12(23), pages 1-18, November.

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