Optimal coordination of wind-hydro-thermal based on water complementing wind

Renewable Energy

Volumn 60, Issue , 2013, Pages 169-178

  Wang, K Y ^a   Luo, X J ^a   Wu, L ^b   Liu, X C ^a  

^a XI'AN JIAOTONG UNIVERSITY   (China)

^b Xianyang Power Supply Bureau   (China)

Author keywords

Hydropower; Particle swarm algorithm; Short term scheduling; Stochastic constraint; Thermal power; Wind energy

Indexed keywords

HYDROPOWER; PARTICLE SWARM ALGORITHM; SHORT-TERM SCHEDULING; STOCHASTIC CONSTRAINTS; THERMAL POWER;

ALGORITHMS; HYDROELECTRIC POWER; SCHEDULING; STOCHASTIC PROGRAMMING; STOCHASTIC SYSTEMS; WATER RESOURCES; WIND POWER;

PARTICLE SWARM OPTIMIZATION (PSO);

ALGORITHM; ENERGY EFFICIENCY; HYDROELECTRIC POWER; OPTIMIZATION; STOCHASTICITY; THERMAL POWER; UNCERTAINTY ANALYSIS; WIND POWER;

EID: 84878845094     PISSN: 09601481     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.renene.2013.04.015     Document Type: Article

References (30)

1. Goransson L., Johnsson F. Dispatch modeling of a regional power generation system-Integrating wind power. Renew Energy 2009, 34:1040-1049.
2. Chen C.-L. Optimal wind-thermal generating unit commitment. IEEE Trans Energy Convers 2008, 22:273-280.
3. Kuo C.-C. Wind energy dispatch considering environmental and economic factors. Renew Energy 2010, 35:2217-2227.
4. Siahkali H., Vakilian M. Stochastic unit commitment of wind farms integrated in power systems. Electr Power Syst Res 2010, 80:1006-1017.
5. Pappala V.S., Erlich I., Rohrig K., Dobschinski J. Astochastic model for the optimal operation of a wind-thermal power system. IEEE Trans Power Syst 2009, 24:940-950.
6. Restrepo J.E., Galiana F.D. Assessing the yearly impact of wind power through a new hybrid deterministic/stochastic unit commitment. IEEE Trans Power Syst 2011, 26:401-410.
7. Miranda V., Hang P.S. Economic dispatch model with fuzzy wind constraints and attitudes of dispatchers. IEEE Trans Power Syst 2005, 20:2143-2145.
8. Wang L.F., Singh C. Balancing risk and cost in fuzzy economic dispatch including wind power penetration based on particle swarm optimization. Electr Power Syst Res 2008, 78:1361-1368.
9. Constantinescu E.M., Zavala V.M., Rocklin M., Lee S., Anitescu M. Acomputational framework for uncertainty quantification and stochastic optimization in unit commitment with wind power generation. IEEE Trans Power Syst 2011, 26:431-441.
10. Li X.H., Jiang C.W. Short-term operation model and risk management for wind power penetrated system in electricity market. IEEE Trans Power Syst 2011, 26:932-939.
11. Hetzer J., Yu D.C., Bhattarai K. An economic dispatch model incorporating wind power. IEEE Trans Energy Convers 2008, 23:603-611.
12. Azizipanah-Abarghooee R., Niknam T., Roosta A., Malekpour A.R. Probabilistic multiobjective wind-thermal economic emission dispatch based on point estimated method. Energy 2012, 37:322-335.
13. Zhou W., Peng Y., Sun H. Optimal wind-thermal coordination dispatch based on risk reserve constraints. Eur Trans Electr Power 2011, 21:740-756.
14. Hedegaard K., Meibom P. Wind power impacts and electricity storage-A time scale perspective. Renew Energy 2012, 37:318-324.
15. Kaldellis J.K., Kavadias K.A. Optimal wind-hydro solution for Aegean Sea islands' electricity-demand fulfillment. Appl Energy 2001, 70:333-354.
16. Kaldellis J.K., Kavadias K., Christinakis E. Evaluation of the wind-hydro energy solution for remote islands. Energy Convers Manag 2001, 42:1105-1120.
17. Dursun B., Alboyaci B., Gokcol C. Optimal wind-hydro solution for the Marmara region of Turkey to meet electric demand. Energy 2011, 36:864-872.
18. Bueno C., Carta J.A. Technical-economic analysis of wind-powered pumped hydrostorage systems, part I: model development. Sol Energy 2005, 78:382-395.
19. Bueno C., Carta J.A. Technical-economic analysis of wind-powered pumped hydrostorage systems, part II: model application to the island of El Hierro. Sol Energy 2005, 78:396-405.
20. Duque A.J., Castronuovo E.D., Sanchez I., Usaola J. Optimal operation of a pumped-storage hydro plant that compensates the imbalances of a wind power producer. Electr Power Syst Res 2011, 81:1767-1777.
21. Connolly D., Lund H., Mathiesen B.V., Pican E., Leahy M. The technical and economic implications of integrating fluctuating renewable energy using energy storage. Renew Energy 2012, 43:47-60.
22. Hoicka C.E., Roewlands I.H. Solar and wind resource complementarity: adcancing options for renewable electricity integration in Ontario, Canada. Renew Energy 2011, 36:97-107.
23. Wang S.J., Shahidehpour S.M., Kirschen D.S., Mokhtari S., Irisarri G.D. Short-term generation scheduling with transmission and environmental constraints using an augmented lagerangian relaxation. IEEE Trans Power Syst 1995, 10:1294-1301.
24. Chang T.J., Tu Y.L. Evaluation of monthly capacity factor of WECS using chronological and probabilistic wind speed data: a case study of Taiwan. Renew Energy 2007, 32:1999-2010.
25. Sheble G.B., Brittig K. Refined genetic algorithm-economic dispatch example. IEEE Trans Power Syst 1995, 10:117-124.
26. Alrashidi M.R., El-Hawary M.E. Asurvey of particle swarm optimization applications in power system operations. Electr Power Compon Syst 2006, 34:1349-1357.
27. Lee K.Y., Park J.-B. Application of particle swarm optimization to economic dispatch problem: advantages and disadvantages. IEEE proceedings of power systems conference and exposition (PSCE), Atlanta, GA 2006, 188-192.
28. Mahor A., Prasad V., Rangnekar S. Economic dispatch using particle swarm optimization: a review. Renew Sustain Energy Rev 2009, 13:2134-2141.
29. Attaviriyanupap P., Kita H., Tanaka E., Hasegawa J. Ahybrid EP and SQP for dynamic economic dispatch with nonsmooth fuel cost function. IEEE Trans Power Syst 2002, 17:411-416.
30. The IEEE Reliability Test System-1996 Areport prepared by the reliability test system task force of the application of probability methods Subcommittee. IEEE Trans Power Syst 1999, 14:1010-1020.