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Firming renewable power with demand response: an end-to-end aggregator business model

Author

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  • Clay Campaigne

    (University of California, Berkeley)

  • Shmuel S. Oren

    (University of California, Berkeley)

Abstract

Environmental concerns have spurred greater reliance on variable renewable energy resources (VERs) in electric generation. Under current incentive schemes, the uncertainty and intermittency of these resources impose costs on the grid, which are typically socialized across the whole system, rather than born by their creators. We consider an institutional framework in which VERs face market imbalance prices, giving them an incentive to produce higher-value energy subject to less adverse uncertainty. In this setting, we consider an “aggregator” that owns the production rights to a VER’s output, and also signs contracts with a population of demand response (DR) participants for the right to curtail them in real time, according to a contractually specified probability distribution. The aggregator bids a day ahead offer into the wholesale market, and is able to offset imbalances between the cleared day-ahead bid and the realized VER production by curtailing DR participants’ consumption according to the signed contracts. We consider the optimization of the aggregator’s end-to-end problem: designing the menu of DR service contracts using contract theory, bidding into the wholesale market, and dispatching DR consistently with the contractual agreements. We do this in a setting in which wholesale market prices, VER output, and participant demand are all stochastic, and possibly correlated.

Suggested Citation

  • Clay Campaigne & Shmuel S. Oren, 2016. "Firming renewable power with demand response: an end-to-end aggregator business model," Journal of Regulatory Economics, Springer, vol. 50(1), pages 1-37, August.
    • Handle: RePEc:kap:regeco:v:50:y:2016:i:1:d:10.1007_s11149-016-9301-y
    DOI: 10.1007/s11149-016-9301-y
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    Cited by:

    1. Vinicius Neves Motta & Miguel F. Anjos & Michel Gendreau, 2023. "Optimal allocation of demand response considering transmission system congestion," Computational Management Science, Springer, vol. 20(1), pages 1-22, December.
    2. Le Cadre, Hélène & Pagnoncelli, Bernardo & Homem-de-Mello, Tito & Beaude, Olivier, 2019. "Designing coalition-based fair and stable pricing mechanisms under private information on consumers’ reservation prices," European Journal of Operational Research, Elsevier, vol. 272(1), pages 270-291.
    3. Daeho Kim & Dong Gu Choi, 2023. "The aggregator’s contract design problem in the electricity demand response market," Operational Research, Springer, vol. 23(1), pages 1-47, March.
    4. Ruokamo, Enni & Kopsakangas-Savolainen, Maria & Meriläinen, Teemu & Svento, Rauli, 2019. "Towards flexible energy demand – Preferences for dynamic contracts, services and emissions reductions," Energy Economics, Elsevier, vol. 84(C).
    5. Keck, Felix & Lenzen, Manfred, 2021. "Drivers and benefits of shared demand-side battery storage – an Australian case study," Energy Policy, Elsevier, vol. 149(C).
    6. Flottmann, Jonty H. & Akimov, Alexandr & Simshauser, Paul, 2022. "Firming merchant renewable generators in Australia’s National Electricity Market," Economic Analysis and Policy, Elsevier, vol. 74(C), pages 262-276.
    7. Hélène Le Cadre & Bernardo Pagnoncelli & Tito Homem-De-Mello & Olivier Beaude, 2018. "Designing Coalition-Based Fair and Stable Pricing Mechanisms Under Private Information on Consumers' Reservation Prices," Working Papers hal-01353763, HAL.
    8. Daeho Kim & Hyungkyu Cheon & Dong Gu Choi & Seongbin Im, 2022. "Operations Research Helps the Optimal Bidding of Virtual Power Plants," Interfaces, INFORMS, vol. 52(4), pages 344-362, July.
    9. Hélène Le Cadre & Bernardo Pagnoncelli & Tito Homem-De-Mello & Olivier Beaude, 2018. "Designing Coalition-Based Fair and Stable Pricing Mechanisms Under Private Information on Consumers' Reservation Prices," Post-Print hal-01353763, HAL.

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    More about this item

    Keywords

    Electricity markets; Demand response; Aggregator; Business model; Renewables integration; Market design; Screening mechanisms;
    All these keywords.

    JEL classification:

    • D11 - Microeconomics - - Household Behavior - - - Consumer Economics: Theory
    • D45 - Microeconomics - - Market Structure, Pricing, and Design - - - Rationing; Licensing
    • D47 - Microeconomics - - Market Structure, Pricing, and Design - - - Market Design
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources

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