Distributed energy management for networked microgrids using online ADMM with regret

IEEE Transactions on Smart Grid

Volumn 9, Issue 2, 2018, Pages 847-856

  Ma, Wann Jiun ^a   Wang, Jianhui ^b   Gupta, Vijay ^c   Chen, Chen ^b  

^a Duke University   (United States)

^b ARGONNE NATIONAL LABORATORY   (United States)

^c University of Notre Dame   (United States)

Author keywords

Alternating direction method of multipliers; Distributed energy resources; Networked microgrids; Online learning; Regret minimization

Indexed keywords

ENERGY MANAGEMENT; ENERGY RESOURCES; SOLAR ENERGY;

ALTERNATING DIRECTION METHOD OF MULTIPLIERS; DISTRIBUTED ENERGY RESOURCES; MICRO GRID; ONLINE LEARNING; REGRET MINIMIZATION;

OPTIMIZATION;

EID: 85032868342     PISSN: 19493053     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSG.2016.2569604     Document Type: Article

References (28)

1. Z. Wang, B. Chen, J. Wang, M. M. Begovic, and C. Chen, "Coordinated energy management of networked microgrids in distribution systems," IEEE Trans. Smart Grid, vol. 6, no. 1, pp. 45-53, Jan. 2015.
2. G. E. Asimakopoulou, A. L. Dimeas, and N. D. Hatziargyriou, "Leaderfollower strategies for energy management of multi-microgrids," IEEE Trans. Smart Grid, vol. 4, no. 4, pp. 1909-1916, Dec. 2013.
3. Northeast Clean Heat and Power Initiative. (2015). Microgrids: An Assessment of the Value, Opportunities and Barriers to Deployment in New York State. [Online]. Available: http://nechpi.org
4. Maryland Energy Administration. (2015). Maryland Resiliency Through Microgrids Task Force Report. [Online]. Available: http://energy.maryland.gov
5. R. Jiang, J. Wang, and Y. Guan, "Robust unit commitment with wind power and pumped storage hydro," IEEE Trans. Power Syst., vol. 27, no. 2, pp. 800-810, May 2012.
6. N. Hatziargyriou, H. Asano, R. Iravani, and C. Marnay, "Microgrids," IEEE Power Energy Mag., vol. 5, no. 4, pp. 78-94, Jul./Aug. 2007.
7. Y. Zhang, N. Gatsis, and G. B. Giannakis, "Robust energy management for microgrids with high-penetration renewables," IEEE Trans. Sustain. Energy, vol. 4, no. 4, pp. 944-953, Oct. 2013.
8. R. Jiang, J. Wang, M. Zhang, and Y. Guan, "Two-stage minimax regret unit commitment," IEEE Trans. Power Syst., vol. 28, no. 3, pp. 2271-2282, Aug. 2013.
9. N. Cesa-Bianchi and G. Lugosi, Prediction, Learning, and Games. Cambridge, U.K.: Cambridge Univ. Press, 2006.
10. E. Hazan, A. Agarwal, and S. Kale, "Logarithmic regret algorithms for online convex optimization," Mach. Learn. J., vol. 69, nos. 2-3, pp. 169-192, 2007.
11. S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, "Distributed optimization and statistical learning via the alternating direction method of multipliers," Found. Trends Mach. Learn., vol. 3, no. 1, pp. 1-122, 2011.
12. E. Dall'Anese, H. Zhu, and G. B. Giannakis, "Distributed optimal power flow for smart microgrids," IEEE Trans. Smart Grid, vol. 4, no. 3, pp. 1464-1475, Sep. 2013.
13. W.-J. Ma, V. Gupta, and U. Topcu, "On distributed charging control of electric vehicles with power network capacity constraints," in Proc. IEEE Amer. Control Conf., Portland, OR, USA, 2014, pp. 4306-4311.
14. L. Jia, L. Tong, and Q. Zhao, "Retail pricing for stochastic demand with unknown parameters: An online machine learning approach," in Proc. IEEE Allerton Conf., Monticello, IL, USA, 2013, pp. 1353-1358.
15. S.-J. Kim and G. B. Giannakis, "Real-time electricity pricing for demand response using online convex optimization," in Proc. IEEE Innov. Smart Grid Technol. Conf., Washington, DC, USA, 2014, pp. 1-5.
16. L. Lu, J. Tu, C.-K. Chau, M. Chen, and X. Lin, "Online energy generation scheduling for microgrids with intermittent energy sources and co-generation," in Proc. ACM SIGMETRICS/Int. Conf. Measur. Model. Comput. Syst., Pittsburgh, PA, USA, 2013, pp. 53-66.
17. B. Narayanaswamy, V. K. Garg, and T. S. Jayram, "Online optimization for the smart (micro) grid," in Proc. 3rd Int. Conf. Future Energy Syst. Where Energy Comput. Commun. Meet, Madrid, Spain, 2012, Art. no. 19.
18. L. L. H. Andrew et al., "A tale of two metrics: Simultaneous bounds on competitiveness and regret," in Proc. ACM SIGMETRICS Int. Conf. Measur. Model. Comput. Syst., Pittsburgh, PA, USA, 2013, pp. 329-330.
19. Office of the General Counsel, U.S. Department of Energy. (2015). Communications Requirements of Smart Grid Technology. [Online]. Available: http://energy.gov/gc/office-general-counsel
20. D. G. Luenberger, Linear and Nonlinear Programming, 2nd ed. Reading, MA, USA: Addison-Wesley, 1984.
21. H. Wang and A. Banerjee, "Online alternating direction method," in Proc. Int. Conf. Mach. Learn., Edinburgh, U.K., 2012, pp. 1119-1126.
22. E. C. Hall and R. M. Willett, "Dynamical models and tracking regret in online convex programming," in Proc. Int. Conf. Mach. Learn., Atlanta, GA, USA, Jun. 2013, pp. 579-587.
23. W.-J. Ma, "Learning and optimization for smart power grids," Ph.D. dissertation, Dept. Elect. Eng., Univ. Notre Dame, Notre Dame, IN, USA, 2015.
24. M. Kraning, E. Chu, J. Lavaei, and S. P. Boyd, "Dynamic network energy management via proximal message passing," Found. Trends Optim., vol. 1, no. 2, pp. 73-126, Jan. 2014.
25. D. Singh, D. Singh, and K. S. Verma, "Multiobjective optimization for DG planning with load models," IEEE Trans. Power Syst., vol. 24, no. 1, pp. 427-436, Feb. 2009.
26. Elia. (2015). Solar-PV Power Generation Data. [Online]. Available: http://www.elia.be/en/grid-data/power-generation
27. M. Grant and S. Boyd. (Sep. 2013). CVX: MATLAB Software for Disciplined Convex Programming Version 2.0 Beta. [Online]. Available: http://cvxr.com/cvx
28. J. Mattingley and S. Boyd, "CVXGEN: A code generator for embedded convex optimization," Optim. Eng., vol. 13, no. 1, pp. 1-27, Mar. 2012.