A stochastic framework for the grid integration of wind power using flexible load approach

Energy Conversion and Management

Volumn 88, Issue , 2014, Pages 985-998

  Heydarian Forushani, E ^a   Moghaddam, M P ^b   Sheikh El Eslami, M K ^b   Shafie Khah, M ^c   Catalao J P S ^c,d,e  

^a IRAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Iran)

^b TARBIAT MODARES UNIVERSITY   (Iran)

^c UNIVERSITY OF BEIRA INTERIOR   (Portugal)

^d INESC ID   (Portugal)

^e INSTITUTO SUPERIOR TÉCNICO   (Portugal)

Author keywords

Demand response programs; Flexible load; Stochastic programming; Wind power integration

Indexed keywords

DEMAND RESPONSE PROGRAMS; FLEXIBLE LOADS; GRID INTEGRATION; STOCHASTIC FRAMEWORK; WIND POWER INTEGRATIONS;

STOCHASTIC PROGRAMMING;

EID: 84907970894     PISSN: 01968904     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.enconman.2014.09.048     Document Type: Article

References (44)

1. Ghaffari R, Venkatesh B. Options based reserve procurement strategy for wind generators - using binomial trees. IEEE Trans Power Syst 2013;28:1063-72.
2. Timilsina GR, van Kooten GC, Narbel PA. Global wind power development: economics and policies. Energy Policy 2013;61:642-52.
3. Maddaloni JD, Rowe AM, van Kooten GC. Wind integration into various generation mixtures. Renew Energy 2009;34:807-14.
4. Rabiee A, Khorramdel H, Aghaei J. A review of energy storage systems in microgrids with wind turbines. Renew Sustain Energy Rev 2013;18:316-26.
5. Jannati M, Hosseinian SH, Vahidi B, Li GJ. A survey on energy storage resources configurations in order to propose an optimum configuration for smoothing fluctuations of future large wind power plants. Renew Sustain Energy Rev 2014;29:158-72.
6. Zafirakis D, Kaldellis JK. Autonomous dual-mode CAES systems for maximum wind energy contribution in remote island networks. Energy Convers Manage 2010;51:2150-61.
7. Loisel R. Power system flexibility with electricity storage technologies: a technical-economic assessment of a large-scale storage facility. Int J Electr Power Energy Syst 2012;42:542-52.
8. Torreglosa JP, García P, Fernández LM, Jurado F. Hierarchical energy management system for stand-alone hybrid system based on generation costs and cascade control. Energy Convers Manage 2014;77:514-26.
9. Ray PK, Mohanty SR, Kishor N. Proportional-integral controller based small-signal analysis of hybrid distributed generation systems. Energy Convers Manage 2011;52:1943-54.
10. Malakar T, Goswami SK, Sinha AK. Impact of load management on the energy management strategy of a wind-short hydro hybrid system in frequency based pricing. Energy Convers Manage 2014;79:200-12.
11. Nikolova S, Causevski A, Al-Salaymeh A. Optimal operation of conventional power plants in power system with integrated renewable energy sources. Energy Convers Manage 2013;65:697-703.
12. Weber C. Adequate intraday market design to enable the integration of wind energy into the European power systems. Energy Policy 2010;38:3155-63.
13. MacGill I. Electricity market design for facilitating the integration of wind energy: experience and prospects with the Australian National Electricity Market. Energy Policy 2010;38:3180-91.
14. Gil E, Aravena I. Evaluating the capacity value of wind power considering transmission and operational constraints. Energy Convers Manage 2014;78:948-55.
15. Papavasiliou A, Oren S, O' Neill RP. Reserve requirements for wind power integration: a scenario based stochastic programming framework. IEEE Trans Power Syst 2011;26:2197-206.
16. Liu G, Tomsovic K. Quantifying spinning reserve in systems with significant wind power penetration. IEEE Trans Power Syst 2012;27:2385-93.
17. Liu W, Hu W, Lund H, Chen Z. Electric vehicles and large-scale integration of wind power - the case of Inner Mongolia in China. Appl Energy 2013;104:445-56.
18. Fazelpour F, Vafaeipour M, Rahbari O, Rosen MA. Intelligent optimization to integrate a plug-in hybrid electric vehicle smart parking lot with renewable energy resources and enhance grid characteristics. Energy Convers Manage 2014;77:250-61.
19. El-Zonkoly A. Intelligent energy management of optimally located renewable energy systems incorporating PHEV. Energy Convers Manage 2014;84:427-35.
20. Pina A, Silva C, Ferrão P. The impact of demand side management strategies in the penetration of renewable electricity. Energy 2012;41:128-37.
21. Critz DK, Busche S, Connors S. Power systems balancing with high penetration renewables: the potential of demand response in Hawaii. Energy Convers Manage 2013;76:609-19.
22. Sioshansi R, Short W. Evaluating the impacts of real-time pricing on the usage of wind generation. IEEE Trans Power Syst 2010;24:516-24.
23. Dietrich K, Latorre JM, Olmos L, Ramos A. Demand response in an isolated system with high wind integration. IEEE Trans Power Syst 2012;27:20-9.
24. Keane A, Tuohy A, Meibom P, Denny E, et al. Demand side resource operation on the Irish power system with high wind power penetration. Energy Policy 2011;39:2925-34.
25. Parvania M, Fotuhi-Firuzabad M. Integrating load reduction into wholesale energy market with application to wind power integration. IEEE Syst J 2012;6:35-45.
26. Yousefi A, Iu HC, Fernand T, Trinh H. An approach for wind power integration using demand side resources. IEEE Trans Sustain Energy 2013;4:917-24.
27. Chua-Liang S, Kirschen D. Quantifying the effect of demand response on electricity markets. IEEE Trans Power Syst 2009;24:1199-207.
28. Walawalkar R, Fernands S, Thakur N, Chevva KR. Evolution and current status of demand response (DR) in electricity markets: insights from PJM and NYISO. Energy 2010;35:1553-60.
29. Albadi MH, El-Saadany EF. A summary of demand response in electricity markets. Electr Power Syst Res 2008;78:1989-96.
30. Aghaei J, Alizadeh MI. Demand response in smart electricity grids equipped with renewable energy sources: a review. Renew Sustain Energy Rev 2013;18:64-72.
31. IEA. Strategic plan for the IEA demand-side management program 2004-2009. [accessed 2009].
32. Aalami HA, Moghaddam MP, Yousefi GR. Demand response modeling considering interruptible/curtailable loads and capacity market programs. Appl Energy 2010;87:243-50.
33. Schweppe FC, Caramanis MC, Tabors RD, Bohn RE. Spot pricing of electricity. Boston (MA): Kluwer; 1988.
34. Baboli PT, Eghbal M, Moghaddam MP, Aalami H. Customer behavior based demand response model. Power and energy society general meeting, IEEE; 2012. p. 1-7.
35. Wang J, Botterud A, Bessa R, Keko H, Carvalho L, et al. Wind power forecasting uncertainty and unit commitment. Appl Energy 2011;88:4014-23.
36. Liu C, Wang J, Botterud A, Zhou Y, Vyas A. Assessment of impacts of PHEV charging patterns on wind-thermal scheduling by stochastic unit commitment. IEEE Trans Smart Grid 2012;3:675-83.
37. Alishahi E, Moghaddam MP, Sheikh-El-Eslami MK. A system dynamics approach for investigating impacts of incentive mechanisms on wind power investment. Renew Energy 2012;37:310-7.
38. Li T, Shahidehpour M. Price-based unit commitment: a case of Lagrangian relaxation versus mixed integer programming, prices. IEEE Trans Power Syst 2005;20:2015-25.
39. IEA Wind Task 24 final report, vol. 1. Integration of wind and hydropower systems. Final technical report; December 2011.
40. Wang C, Shahidehpour M. Effects of ramp-rate limits on unit commitment and economic dispatch. IEEE Trans Power Syst 1993;8:1341-50.
41. Behrangrad M, Sugihara H, Funaki T. Effect of optimal spinning reserve requirement on system pollution emission considering reserve supplying demand response in the electricity market. Appl Energy 2011;88:2548-58.
42. Karki R, Hu P, Billinton R. A simplified wind power generation model for reliability evaluation. IEEE Trans Energy Convers 2006;21:533-40.
43. Amjady N, Aghaei J, Shayanfar HA. Stochastic multi objective market clearing of joint energy and reserves auctions ensuring power system security. IEEE Trans Power Syst 2009;24:1841-54.
44. Sutiene K, Makackas D, Pranevicius H. Multistage K-means clustering for scenario tree construction. Informatica 2010;21:123-38.