Real-time deferrable load control: Handling the uncertainties of renewable generation

e-Energy 2013 - Proceedings of the 4th ACM International Conference on Future Energy Systems

Volumn , Issue , 2013, Pages 113-124

  Gan, Lingwen ^a   Wierman, Adam ^a   Topcu, Ufuk ^b   Chen, Niangjun ^a   Low, Steven H ^a  

^a CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

deferrable load control; demand response; model predictive control; smart grid

Indexed keywords

DEMAND RESPONSE; ELECTRICITY-CONSUMPTION; RANDOM FLUCTUATION; REAL-TIME DEMAND RESPONSE; RENEWABLE GENERATION; RESIDENTIAL LOADS; SMART GRID; TRACE-BASED SIMULATION;

ALGORITHMS; DOMESTIC APPLIANCES; ELECTRIC VEHICLES; MODEL PREDICTIVE CONTROL; PREDICTIVE CONTROL SYSTEMS;

AGGREGATES;

EID: 84878653533     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2487166.2487179     Document Type: Conference Paper

References (31)

1. S. Acha, T. C. Green, and N. Shah. Effects of optimised plug-in hybrid vehicle charging strategies on electric distribution network losses. In IEEE PES Transmission and Distribution Conference and Exposition, pages 1-6, 2010.
2. D. J. Aigner and J. G. Hirschberg. Commercial/industrial customer response to time-of-use electricity prices: Some experimental results. The RAND Journal of Economics, 16(3):341-355, 1985.
3. Alberta Electric System Operator. Wind power / ail data, 2009. http://www.aeso.ca/gridoperations/20544.html.
4. J.-Y. L. Boudec and D.-C. Tomozei. Satisfiability of elastic demand in the smart grid. arXiv preprint arXiv:1011.5606, 2010.
5. California Public Utilities Commission. Zero net energy action plan, 2008. http://www.cpuc.ca.gov/NR/rdonlyres/6C2310FE-AFE0-48E4-AF03-530A99D28FCE/ 0/ZNEActionPlanFINAL83110.pdf.
6. M. Caramanis and J. Foster. Management of electric vehicle charging to mitigate renewable generation intermittency and distribution network congestion. In IEEE CDC, pages 4717-4722, 2009.
7. L. Chen, N. Li, S. H. Low, and J. C. Doyle. Two market models for demand response in power networks. In IEEE SmartGridComm, pages 397-402, 2010.
8. S. Chen and L. Tong. iems for large scale charging of electric vehicles: architecture and optimal online scheduling. In IEEE SmartGridComm, pages 629-634, 2012.
9. A. Conejo, J. Morales, and L. Baringo. Real-time demand response model. IEEE Transactions on Smart Grid, 1(3):236-242, 2010.
10. S. Deilami, A. Masoum, P. Moses, and M. Masoum. Real-time coordination of plug-in electric vehicle charging in smart grids to minimize power losses and improve voltage profile. IEEE Transactions on Smart Grid, 2(3):456-467, 2011.
11. Department of Energy. The smart grid: an introduction, 2008. http://energy.gov/sites/prod/files/oeprod/DocumentsandMedia/DOE-SG-Book-Single- Pages%281%29.pdf.
12. Department of Energy. One million electric vehicles by 2015, 2011. http://www1.eere.energy.gov/vehiclesandfuels/pdfs/1-million-electric-vehicles- rpt.pdf.
13. E. A. Feinberg and D. Genethliou. Load forecasting. In Applied Mathematics for Restructured Electric Power Systems, Power Electronics and Power Systems, pages 269-285. Springer US, 2005.
14. L. Gan, U. Topcu, and S. H. Low. Optimal decentralized protocol for electric vehicle charging. In IEEE CDC, pages 5798-5804, 2011.
15. L. Gan, U. Topcu, and S. H. Low. Stochastic distributed protocol for electric vehicle charging with discrete charging rate. In IEEE PES General Meeting, pages 1-8, 2012.
16. L. Gan, A. Wierman, U. Topcu, N. Chen, and S. H. Low. Real-time deferrable load control: handling the uncertainties of renewable generation, 2013. Technical report, available at http://www.its.caltech.edu/∼lgan/index. html.
17. G. Giebel, R. Brownsword, G. Kariniotakis, M. Denhard, and C. Draxl. The State-Of-The-Art in Short-Term Prediction of Wind Power. ANEMOS.plus, 2011.
18. S. C. Graves, D. B. Kletter, and W. B. Hetzel. A dynamic model for requirements planning with application to supply chain optimization. Manufacturing & Service Operation Management, 1(1):50-61, 1998.
19. S. C. Graves, H. C. Meal, S. Dasu, and Y. Qiu. Two-stage production planning in a dynamic environment, 1986. http://web.mit.edu/sgraves/www/papers/ GravesMealDasuQiu.pdf.
20. N. Hatziargyriou, H. Asano, R. Iravani, and C. Marnay. Microgrids. IEEE Power and Energy Magazine, 5(4):78-94, 2007. (Pubitemid 47171494)
21. Y.-Y. Hsu and C.-C. Su. Dispatch of direct load control using dynamic programming. IEEE Transactions on Power Systems, 6(3):1056-1061, 1991.
22. M. Ilic, J. Black, and J. Watz. Potential benefits of implementing load control. In IEEE PES Winter Meeting, volume 1, pages 177-182, 2002.
23. N. Li, L. Chen, and S. H. Low. Optimal demand response based on utility maximization in power networks. In IEEE PES General Meeting, pages 1-8, 2011.
24. Q. Li, T. Cui, R. Negi, F. Franchetti, and M. D. Ilic. On-line decentralized charging of plug-in electric vehicles in power systems. arXiv:1106.5063, 2011.
25. Z. Ma, D. Callaway, and I. Hiskens. Decentralized charging control for large populations of plug-in electric vehicles. In IEEE CDC, pages 206-212, 2010.
26. K. Mets, T. Verschueren, W. Haerick, C. Develder, and F. De Turck. Optimizing smart energy control strategies for plug-in hybrid electric vehicle charging. In IEEE/IFIP NOMS Wksps, pages 293-299, 2010.
27. National Institute of Standards and Technology. Nist framework and roadmap for smart grid interoperability standards, 2010. http://www.nist.gov/ public-affairs/releases/upload/smartgrid-interoperability-final.pdf.
28. Southern California Edison. 2012 static load profiles, 2012. http://www.sce.com/005-regul-info/eca/DOMSM12.DLP.
29. A. Subramanian, M. Garcia, A. Dominguez-Garcia, D. Callaway, K. Poolla, and P. Varaiya. Real-time scheduling of deferrable electric loads. In ACC, pages 3643-3650, 2012.
30. Wikipedia. Krasovskii-lasalle principle. http://en.wikipedia.org/wiki/ Krasovskii-LaSalle-principle.
31. Wikipedia. Wiener filter. http://en.wikipedia.org/wiki/Wiener-filter.