Multi-objective optimal energy consumption scheduling in smart grids

IEEE Transactions on Smart Grid

Volumn 4, Issue 1, 2013, Pages 341-348

  Salinas, Sergio ^a   Li, Ming ^a   Li, Pan ^a  

^a MISSISSIPPI STATE UNIVERSITY   (United States)

Author keywords

Energy consumption scheduling; evolutionary algorithms; multi objective optimization

Indexed keywords

CONSTRAINED MULTI-OBJECTIVE OPTIMIZATIONS; EVOLUTIONARY ALGORITHMS (EAS); EXTENSIVE SIMULATIONS; GENERATION CAPACITY; MULTI OBJECTIVE; PEAK LOAD DEMAND; SECURITY MARGINS; TWO WAY COMMUNICATIONS;

COSTS; ENERGY UTILIZATION; EVOLUTIONARY ALGORITHMS; MULTIOBJECTIVE OPTIMIZATION; SCHEDULING;

SMART POWER GRIDS;

EID: 84875050140     PISSN: 19493053     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSG.2012.2214068     Document Type: Article

References (17)

1. G. Strbac, "Demand side management: Benefits and challenges," Energy Policy, vol. 36, no. 12, pp. 4419-4426, November 2008
2. 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 Trans. Smart Grid, vol. 1, no. 3, pp. 320-331, Dec. 2010
3. T. Agarwal and S. Cui, "Noncooperative games for autonomous consumer load balancing over smart grid," CoRR, 2011 [Online]. Available: http://arxiv.org/abs/1104.3802
4. P. Samadi, R. Schober, and V. Wong, "Optimal energy consumption scheduling using mechanism design for the future smart grid," in Proc. IEEE Int. Conf. Smart Grid Commun. (SmartGridComm), Brussels, Belgium, Oct. 2011
5. D. Li, S. Jayaweera, and A. Naseri, "Auctioning game based demand response scheduling in smart grid," in Online Conf. Green Commun. (GreenCom), Sep. 2011
6. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: Nsga-ii," IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182-197, Apr. 2002 (Pubitemid 34555372)
7. J. Knowles and D. Corne, "The Pareto archived evolution strategy: A new baseline algorithm for Pareto multiobjective optimisation," in Proc. Congr. Evol. Comput. (CEC), Washington, DC, Jul. 1999
8. E. Zitzler,M. Laumanns, and L. Thiele, "Spea2: Improving the strength Pareto evolutionary algorithm," TIK-Rep. No. 103, 2001
9. J. Horn, N. Nafpliotis, and D. Goldberg, "A niched Pareto genetic algorithm for multiobjective optimization," in Proc. 1st IEEE Conf. Evol. Comput., Orlando, FL, Jun. 1994, pp. 82-87
10. D. W. Corne, J. D. Knowles, and M. J. Oates, "The Pareto envelopebased selection algorithm for multiobjective optimization," in Proc. Parallel Problem Solving From Nat. Conf., Paris, France, Sep. 2000, pp. 839-848
11. D. A. V. Veldhuizen and G. B. Lamont, "Multiobjective optimization with messy genetic algorithms," in Proc. Symp. Appl. Comput., Villa Olmo, Italy, Mar. 2000
12. U.S. Dept. of Energy, "Communications requirements of smart grid technologies," Oct. 5, 2010
13. C. A. C. Coello, G. B. Lamont, and D. A. V. Veldhuizen, Evolutionary Algorithms for Solving Multi-Objective Problems. New York: Springer, 2007
14. R. B. Agrawal and K. Deb, "Simulated binary crossover for continuous search space," Indian Institute of Technology, Convenor Tech. Rep., 1994
15. E. Zitzler, M. Laumanns, and S. Bleuler, "A tutorial on evolutionary multiobjective optimization," inMetaheuristics for Multiobjective Optimisation, ser. Lecture Notes in Economics and Mathematical Systems. New York: Springer, 2003, vol. 535, pp. 3-38 (Pubitemid 38203475)
16. K. Deb, M. Mohan, and S. Mishra, "Evaluating the Domination based multi-objective evolutionary algorithm for a quick computation of Pareto-optimal solutions," Evol. Comput., vol. 13, no. 4, pp. 501-525, Winter, 2005 (Pubitemid 43955280)
17. M. Laumanns, L. Thiele, E. Zitzler, and K. Deb, "Archiving with guaranteed convergence and diversity in multi-objective optimization," in Proc. Genet. Evol. Comput. Conf., New York, Jul. 2002.