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Energy Coupon: A Mean Field Game Perspective on Demand Response in Smart Grids

Published: 15 June 2015 Publication History

Abstract

No abstract available.

References

[1]
Electric Reliability Council of Texas (ERCOT). Data set available at http://www.ercot.com/.
[2]
Pecan Street. Data set available at https://dataport.pecanstreet.org/.
[3]
J. M. Lasry and P. Lions. Mean field games. Japanese Journal of Mathematics, 2(1):229--260, 2007.
[4]
J. Li, B. Xia, X. Geng, H. Ming, S. Shakkottai, V. Subramanian, and L. Xie. Energy coupon: A mean field game perspective on demand response in smart grids. Arxiv preprint arXiv:1503.02951, 2015.
[5]
B. Prabhakar. Designing large-scale nudge engines. In Proceedings of the ACM SIGMETRICS, 2013.

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  • (2023)Adapt Load Behavior as Technology Agnostic SolutionData Science and Applications for Modern Power Systems10.1007/978-3-031-29100-5_4(45-125)Online publication date: 28-Feb-2023
  • (2022)Reinforcement Learning for Mean-Field GameAlgorithms10.3390/a1503007315:3(73)Online publication date: 22-Feb-2022
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Published In

cover image ACM Conferences
SIGMETRICS '15: Proceedings of the 2015 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems
June 2015
488 pages
ISBN:9781450334860
DOI:10.1145/2745844
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Publication History

Published: 15 June 2015

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Author Tags

  1. demand response
  2. game theory
  3. incentives
  4. lottery scheme
  5. mean field games
  6. smart grids

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SIGMETRICS '15 Paper Acceptance Rate 32 of 239 submissions, 13%;
Overall Acceptance Rate 459 of 2,691 submissions, 17%

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Cited By

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  • (2023)OpenGridGym: An Open-Source AI-Friendly Toolkit for Distribution Market SimulationIEEE Transactions on Smart Grid10.1109/TSG.2022.321324014:2(1555-1565)Online publication date: Mar-2023
  • (2023)Adapt Load Behavior as Technology Agnostic SolutionData Science and Applications for Modern Power Systems10.1007/978-3-031-29100-5_4(45-125)Online publication date: 28-Feb-2023
  • (2022)Reinforcement Learning for Mean-Field GameAlgorithms10.3390/a1503007315:3(73)Online publication date: 22-Feb-2022
  • (2022)Multi-Agent Learning via Markov Potential Games in Marketplaces for Distributed Energy Resources2022 IEEE 61st Conference on Decision and Control (CDC)10.1109/CDC51059.2022.9992762(6350-6357)Online publication date: 6-Dec-2022
  • (2018)How does coal-electricity price linkage impact on the profit of enterprises in China? Evidence from a Stackelberg game modelResources, Conservation and Recycling10.1016/j.resconrec.2016.09.016129(383-391)Online publication date: Feb-2018
  • (2017)Incentivizing Sharing in Realtime D2D Streaming NetworksIEEE/ACM Transactions on Networking10.1109/TNET.2016.256202825:1(3-17)Online publication date: 1-Feb-2017
  • (2023)Optimal Bilevel Lottery Design for Multiagent SystemsIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2023.329664353:11(7177-7187)Online publication date: Nov-2023
  • (2020)Prediction and assessment of demand response potential with coupon incentives in highly renewable power systemsProtection and Control of Modern Power Systems10.1186/s41601-020-00155-x5:1Online publication date: 6-Apr-2020
  • (2020)A Hierarchical Approach to Multienergy Demand Response: From Electricity to Multienergy ApplicationsProceedings of the IEEE10.1109/JPROC.2020.2983388108:9(1457-1474)Online publication date: Sep-2020
  • (2019)Communication Networks: Pricing, Congestion Control, Routing, and SchedulingHandbook of Dynamic Game Theory10.1007/978-3-319-27335-8_29-2(1-41)Online publication date: 2-Dec-2019
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