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Constrained tâtonnement for fast and incentive compatible distributed demand management in smart grids

Published: 21 May 2013 Publication History

Abstract

Growing fuel costs, environmental awareness, government directives, an aggressive push to deploy Electric Vehicles (EVs) (a single EV consumes the equivalent of 3 to 10 homes) have led to a severe strain on a grid already on the brink. Maintaining the stability of the grid requires automatic agent based control of these loads and rapid coordination between them. In the literature, a number of iterative pricing, signaling and tâtonnement (or bargaining) approaches have been proposed to allow smart homes, storage devices and the autonomous agents that control them to be responsive to the state of the grid in a distributed manner. These existing approaches are not scalable due to slow convergence and moreover the approaches are not incentive compatible. In this paper, we present a tâtonnement framework for resource allocation among intelligent agents in the smart grid, that non-trivially generalizes past work in this area. Our approach based on the work in server load balancing involves communicating carefully chosen, centrally verifiable constraints on the set of actions available to agents and cost functions, leading to distributed, incentive compatible protocols that converge in a constant number of iterations, independent of the number of users. These protocols can work on the top of prior approaches and result in a substantial speed-up, while ensuring that it is in the best interests of the agents to be truthful. We demonstrate this theoretically and through extensive simulations for three important scenarios that have been discussed in the literature. We extend the techniques to account for capacity limits in each time slot, the EV charging problem and the distributed storage control problem. We establish the generality and usefulness of this technique and making the case that it should be incorporated into future smart grid protocols.

References

[1]
M. Albadi and E. El-Saadany. Demand response in electricity markets: An overview. In IEEE Power Engineering Society General Meeting, 2007.
[2]
R. Anderson and S. Fuloria. On the security economics of electricity metering. Proceedings of the WEIS, 2010.
[3]
O. Ardakanian, C. Rosenberg, and S. Keshav. Real-time distributed congestion control for electrical vehicle charging. Greenmetrics, 2012.
[4]
B. Awerbuch, Y. Azar, and R. Khandekar. Fast load balancing via bounded best response. In SODA, pages 314--322, 2008.
[5]
P. Barker and R. De Mello. Determining the impact of distributed generation on power systems. i. radial distribution systems. In IEEE Power Engineering Society Summer Meeting, volume 3, pages 1645--1656 vol. 3, 2000.
[6]
S. Barker, A. Mishra, D. Irwin, E. Cecchet, P. Shenoy, and J. Albrecht. Smart*: An open data set and tools for enabling research in sustainable homes. 2012.
[7]
S. Borenstein. The trouble with electricity markets: Understanding California's restructuring disaster. The Journal of Economic Perspectives, 16(1):191--211, 2002.
[8]
T. Carpenter, S. Singla, P. Azimzadeh, and S. Keshav. The impact of electricity pricing schemes on storage adoption in ontario. In e-Eenergy, 2012.
[9]
H. Chao. Competitive electricity markets with consumer subscription service in a smart grid. Journal of Regulatory Economics, pages 1--26, 2012.
[10]
K. Clement-Nyns, E. Haesen, and J. Driesen. The impact of charging plug-in hybrid electric vehicles on a residential distribution grid. Power Systems, IEEE Transactions on, 25(1):371--380, 2010.
[11]
EPRI and NRDC. Environmental assessment of plug-in hybrid electric vehicles. volume 1: Nationwide greenhouse gas emissions. Technical Report 1015 325, July 2007.
[12]
H. Farhangi. The path of the smart grid. IEEE Power and Energy Magazine, 8(1):18--28, 2010.
[13]
S. Ghosh. Electricity consumption and economic growth in india. Energy Policy, 30(2):125--129, 2002.
[14]
S. Hadley and A. Tsvetkova. Potential impacts of plug-in hybrid electric vehicles on regional power generation. The Electricity Journal, 22(10), 2009.
[15]
C. Harris. Electricity Markets: Pricing, Structures and Economics. Wiley, Sussex, England, 2006.
[16]
L. Huang, J. Walrand, and K. Ramchandran. Optimal smart grid tariffs. In Information Theory and Applications Workshop (ITA), pages 212--220, 2012.
[17]
M. Ilic, L. Xie, and J. Joo. Efficient coordination of wind power and price-responsive demand Part I: Theoretical foundations. IEEE Transactions on Power Systems, 26(4):1875--1884, 2011.
[18]
International Energy Agency. The power to choose - enhancing demand response in liberalised electricity markets findings of IEA demand response project. 2003.
[19]
S. Keshav and C. Rosenberg. How internet concepts and technologies can help green and smarten the electrical grid. ACM SIGCOMM Computer Communication Review, 41(1):109--114, 2011.
[20]
M. Kintner-Meyer, K. Schneider, and R. Pratt. Impacts assessment of plug-in hybrid vehicles on electric utilities and regional us power grids, Part 1: Technical analysis. Pacific Northwest National Laboratory, 2007.
[21]
P. Kundur, N. Balu, and M. Lauby. Power system stability and control, volume 4. McGraw-hill New York, 1994.
[22]
N. Li, L. Chen, and S. Low. Optimal demand response based on utility maximization in power networks. In IEEE Power and Energy Society General Meeting, pages 1--8, july 2011.
[23]
N. A. Lynch. Distributed algorithms. Morgan Kaufmann, 1996.
[24]
A. Mohsenian-Rad, V. Wong, J. Jatskevich, R. Schober, and A. Leon-Garcia. Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Transactions on Smart Grid, 1(3), 2010.
[25]
R. Myerson. Game theory: analysis of conflict. Harvard University Press, 1997.
[26]
Y. Narahari, D. Garg, R. Narayanam, and H. Prakash. Game theoretic problems in network economics and mechanism design solutions. Springer, 2009.
[27]
S. Ramchurn, P. Vytelingum, A. Rogers, and N. Jennings. Agent-based control for decentralised demand side management in the smart grid. In AAMAS, pages 5--12, February 2011.
[28]
I. Richardson, M. Thomson, D. Infield, and C. Clifford. Domestic electricity use: A high-resolution energy demand model. Energy and Buildings, 42(10):1878--1887, 2010.
[29]
W. Saad, Z. Han, H. V. Poor, and T. Basar. Game theoretic methods for the smart grid. CoRR, abs/1202.0452, 2012.
[30]
V. Sood, D. Fischer, J. Eklund, and T. Brown. Developing a communication infrastructure for the smart grid. In Electrical Power & Energy Conference (EPEC), 2009 IEEE, pages 1--7. Ieee, 2009.
[31]
G. Strbac. Demand side management: Benefits and challenges. Energy Policy, 36(12):4419 -- 4426, 2008.
[32]
R. Urgaonkar, B. Urgaonkar, M. Neely, and A. Sivasubramaniam. Optimal power cost management using stored energy in data centers. In Proceedings of the ACM SIGMETRICS, pages 221--232. ACM, 2011.
[33]
P. Vytelingum, T. Voice, S. Ramchurn, A. Rogers, and N. Jennings. Agent-based micro-storage management for the smart grid. In AAMAS, pages 39--46, 2010.
[34]
D. Walker. Walras's theories of tatonnement. The Journal of Political Economy, 95(4):758--774, 1987.
[35]
L. Xie and M. Ilic. Model predictive dispatch in electric energy systems with intermittent resources. In IEEE International Conference on Systems, Man and Cybernetics, pages 42 --47, oct. 2008.
[36]
T. Zhu, A. Mishra, D. Irwin, N. Sharma, P. Shenoy, and D. Towsley. The case for efficient renewable energy management for smart homes. Proceedings of the Third Workshop on Embedded Sensing Systems for Energy-efficiency in Buildings (BuildSys), November 2011.

Cited By

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  • (2023)A novel demand response model and method for peak reduction in smart gridsProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/389(3497-3504)Online publication date: 19-Aug-2023
  • (2023)Tâtonnement in Homothetic Fisher MarketsProceedings of the 24th ACM Conference on Economics and Computation10.1145/3580507.3597746(760-781)Online publication date: 9-Jul-2023
  • (2021)Designing Bounded Min-Knapsack Bandits Algorithm for Sustainable Demand ResponsePRICAI 2021: Trends in Artificial Intelligence10.1007/978-3-030-89188-6_1(3-17)Online publication date: 25-Oct-2021
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    cover image ACM Conferences
    e-Energy '13: Proceedings of the fourth international conference on Future energy systems
    January 2013
    306 pages
    ISBN:9781450320528
    DOI:10.1145/2487166
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    Published: 21 May 2013

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    Author Tags

    1. demand management
    2. distributed algorithms
    3. electric vehicle
    4. smart distributed system

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    View all
    • (2023)A novel demand response model and method for peak reduction in smart gridsProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/389(3497-3504)Online publication date: 19-Aug-2023
    • (2023)Tâtonnement in Homothetic Fisher MarketsProceedings of the 24th ACM Conference on Economics and Computation10.1145/3580507.3597746(760-781)Online publication date: 9-Jul-2023
    • (2021)Designing Bounded Min-Knapsack Bandits Algorithm for Sustainable Demand ResponsePRICAI 2021: Trends in Artificial Intelligence10.1007/978-3-030-89188-6_1(3-17)Online publication date: 25-Oct-2021
    • (2015)A Review of Incentive Based Demand Response Methods in Smart Electricity GridsInternational Journal of Monitoring and Surveillance Technologies Research10.4018/IJMSTR.20151001043:4(62-73)Online publication date: 1-Oct-2015

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