A novel energy service model and optimal scheduling algorithm for residential distributed energy resources

Electric Power Systems Research

Volumn 81, Issue 12, 2011, Pages 2155-2163

  Pedrasa, Michael Angelo A ^a   Spooner, Ted D ^b   MacGill, Iain F ^b  

^a UNIVERSITY OF THE PHILIPPINES   (Philippines)

^b UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

Co evolutionary optimization; Distributed energy resources; Electricity tariff; Energy services; Home automation; Particle swarm optimization

Indexed keywords

DISTRIBUTED ENERGY RESOURCES; ELECTRICITY TARIFF; ENERGY SERVICES; HOME AUTOMATION; PARTICLE SWARM;

DECISION SUPPORT SYSTEMS; MATHEMATICAL MODELS; PARTICLE SWARM OPTIMIZATION (PSO); SCHEDULING ALGORITHMS;

ENERGY RESOURCES;

EID: 80052946019     PISSN: 03787796     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.epsr.2011.06.013     Document Type: Article

References (29)

1. J. Randolph, and G.M. Masters Energy for Sustainability: Technology, Planning, Policy 2008 Island Press Washington
2. A. Lovins, E. Datta, T. Feiler, K. Rabago, J. Swisher, A. Lehmann, and K. Wicker Small is Profitable: The Hidden Economic Benefits of Making Electrical Resources the Right Size 2002 Rocky Mountain Institute Snowmass, CO
3. J.A. Pecas Lopes, N. Hatziargyriou, J. Mutale, P. Djapic, and N. Jenkins Integrating distributed generation into electric power systems: a review of drivers, challenges and opportunities Electr. Power Syst. Res. 77 2007 1189 1203 (Pubitemid 46654694)
4. M. Ilic, J.W. Black, and M. Prica Distributed electric power systems of the future: institutional and technological drivers for near-optimal performance Electr. Power Syst. Res. 77 2007 1160 1177 (Pubitemid 46661893)
5. M.A. Pedrasa, E.D. Spooner, and I.F. MacGill Improved energy services provision through the intelligent control of distributed energy resources 2009 IEEE Bucharest Powertech Conference Bucharest, Romania July 2009
6. R. Haas, N. Nakicenovic, A. Ajanovic, T. Faber, L. Kranzi, A. Muller, and G. Resch Towards sustainability of energy systems: a primer on how to apply the concept of energy services to identify necessary trends and policies Energy Policy 36 2008 4012 4021
7. G.M. Masters Renewable and Efficient Electric Power Systems 2004 John Wiley & Sons, Inc. Hoboken, New Jersey
8. IBM ILOG CPLEX Optimizer: High-Performance Mathematical Programming Engine. See also: http://www-01.ibm.com/software/integration/optimization/cplex- optimizer/.
9. General Algebraic Modeling System Home Page. See also: http://www.gams.com/.
10. J. Kennedy, and R. Eberhart Particle swarm optimization Proceeding of the 1995 IEEE International Conference on Neural Networks, vol. 4 1995 1942 1948
11. J. Kennedy, and R. Eberhart A discrete binary version of the particle swarm algorithm Proceedings of the 1997 IEEE International Conference on Systems, Man and Cybernetics, vol. 5 1997 4104 4108
12. Y. Del Valle, G.K. Venayagamoorthy, S. Mohagheghi, J-C. Hernandez, and R.G. Harley Particle swarm optimization: basic concepts, variants and applications in power systems IEEE Trans. Evol. Comput. 12 2008 171 195 (Pubitemid 351546693)
13. A.D. Yucekaya, J. Valenzuela, and G. Dozier Strategic bidding in electricity markets using particle swarm optimization Electr. Power Syst. Res. 79 2009 335 345
14. M. El-Telbany, and F. El-Karmi Short-term forecasting of Jordanian electricity demand using particle swarm optimization Electr. Power Syst. Res. 78 2008 425 433 (Pubitemid 351173546)
15. M.A. Pedrasa, E.D. Spooner, and I.F. MacGill Scheduling of demand side resources using binary particle swarm optimization IEEE Trans. Power Syst. 24 2009 1173 1181
16. P. Reche Lopez, M. Gomez Gonzalez, N. Ruiz Reyes, and F. Jurado Optimization of biomass fueled systems for distributed power generation using Particle Swarm Optimization Electr. Power Syst. Res. 78 2008 1448 1455
17. T. Niknam, A.M. Ranjbar, A.R. Shirani, B. Mozafari, and A. Ostadi Optimal operation of distribution system with regard to distributed generation: a comparison of evolutionary methods Conference Record of the Fortieth IAS Annual Meeting Industry Applications Conference, vol. 4 2005 2690 2697 (Pubitemid 44055711)
18. E. Elbeltagi, T. Hegazy, and D. Grierson Comparison among five evolutionary-based optimization algorithms Adv. Eng. Inform. 19 2005 43 53 (Pubitemid 41000265)
19. S.P. Karthikeyan, K. Palanisamy, L.J. Varghese, I.J. Raglend, and D.P. Kothari Comparison of intelligent techniques to solve load economic dispatch problem with line flow constraints Proceedings of the IEEE International Advanced Computing Conference 2009 446 452
20. Y. Liu, X. Yao, Q. Zhao, and T. Higuchi Scaling up fast evolutionary programming with cooperative coevolution Proceedings of the 2001 Congress on Evolutionary Computation, vol. 2 2001 1101 1108 (Pubitemid 32806262)
21. F. van den Bergh, and A.P. Engelbrecht A cooperative approach to particle swarm optimization IEEE Trans. Evol. Comput. 8 2004 225 239
22. Energy Australia Regulated Residential Energy Rates. Sydney, Australia. See also: http://www.energy.com.au/energy/ea.nsf/Content/ NSW+Your+energy+agreement+ResidentialYA.
23. Department of Primary Industries: Victorian Feed-in Tariffs. Melbourne, Australia. See also: http://www.dpi.vic.gov.au/dpi/dpinenergy.nsf/Home+Page/ Energy~Home+Page?open.
24. C. Amos, Advanced tariff design. Workshop on Advance Metering Infrastructure, Sydney, Australia, 2006. See also: http://www.ceem.unsw.edu.au/ windworkshop/AMIworkshop3-1ChrisAmos.pdf.
25. H. Sauer, R. Howell, and W. Coad Principles of Heating, Ventilating and Air Conditioning 2001 ASHRAE Atlanta, USA
26. L.D. Ha, S. Ploix, E. Zamai, and M. Jacomino Tabu search for the optimization of household energy consumption Proceedings of the 2006 IEEE International Conference on Information Reuse and Integration 2006 86 92 (Pubitemid 47159172)
27. Rheem Hot Water Manual. See also: http://www.rheem.com.au/manuals.asp? view=commercial.
28. F. van den Bergh, and A.P. Engelbrecht A study of particle swarm optimization trajectories Inf. Sci. 176 2006 937 971 (Pubitemid 43190068)
29. C.A. Coello Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art Comput. Methods Appl. Mech. Eng. 191 11-12 2002 1245 1287 (Pubitemid 34099386)