Optimal scheduling of wind-thermal power system using clustered adaptive teaching learning based optimization

Electrical Engineering

Volumn 99, Issue 2, 2017, Pages 535-550

  Reddy, S Surender ^a  

^a Woosong University   (South Korea)

Author keywords

Demand response offers; Renewable energy resources; Spinning reserves; Unit commitment; Weibull distribution; Wind energy

Indexed keywords

ALGORITHMS; COSTS; ELECTRIC POWER GENERATION; ENERGY RESOURCES; OPTIMIZATION; PROBABILITY DENSITY FUNCTION; SCHEDULING; STOCHASTIC SYSTEMS; WEIBULL DISTRIBUTION; WIND; WIND EFFECTS; WIND POWER;

DEMAND RESPONSE; OBJECTIVE FUNCTIONS; SPINNING RESERVES; TEACHING-LEARNING-BASED OPTIMIZATIONS; UNIT-COMMITMENT; WIND ENERGY GENERATORS; WIND GENERATOR SYSTEMS; WIND-THERMAL POWER SYSTEMS;

RENEWABLE ENERGY RESOURCES;

EID: 84975503656     PISSN: 09487921     EISSN: 14320487     Source Type: Journal    
DOI: 10.1007/s00202-016-0382-5     Document Type: Article

References (53)

1. Ela E, Kirby B, Lannoye E, Milligan M, Flynn D, Zavadil B, Malley MO Evolution of operating reserve determination in wind power integration studies. Available [online]:www.nrel.gov/docs/fy11osti/49100
2. Liu K, Zhong J (2010) Generation dispatch considering wind energy and system reliability. In: IEEE power and energy society general meeting, pp 1–7
3. Song Z, Goel L, Wang P (2005) Optimal spinning reserve allocation in deregulated power systems. IEE Gen Trans Distriub 152(4):483–488
4. Billinton R, Karki R (1999) Capacity reserve assessment using system well-being analysis. IEEE Trans Power Syst 14:433–438
5. PJM (2014) PJM Manual 11: energy and ancillary services market operations. Available [online]: http://www.pjm.com/~/media/documents/manuals/m11.ashx
6. New York Independent System Operator: Ancillary Services Manual, Revision 2.0. Available [online]: http://www.nyiso.com/services/documents/manuals/index.html
7. Wang P, Zareipour H, Rosehart WD (2011) Characteristics of the prices of operating reserves and regulation services in competitive electricity markets. Energy Policy 39:3210–3221
8. California ISO (2002) Ancilliary services overview. Available [online]: http://www.caiso.com/clientserv/settlements/SettlementsGuide/index.html
9. Walawalkar R, Fernands S, Thakur N, Chevva KR (2010) Evolution and current status of demand response (DR) in electricity markets: Insights from PJM and NYISO. Energy 35:1553–1560
10. Parvania M, Fotuhi-Firuzabad M (2010) Demand response scheduling by stochastic SCUC. IEEE Trans Smart Grid 1(1):89–98
11. Parvania M, Fotuhi-Firuzabad M (2012) Integrating load reduction into wholesale energy market with application to wind power integration. IEEE Syst J 6(1):35–45
12. Wang J, Redondo NE, Galiana FD (2003) Demand side reserve offers in joint energy/reserve electricity markets. IEEE Trans Power Syst 18(4):1300–1306
13. Karangelos E, Bouffard F (2012) Towards full integration of demand-side resources in joint forward energy/reserve electricity markets. IEEE Trans Power Syst 27(1):280–289
14. Wong S, Fuller JD (2007) Pricing energy and reserves using stochastic optimization in an alternative electricity market. IEEE Trans Power Syst 22(2):631–638
15. Lakshmi K, Vasantharathna S (2014) Gencos wind-thermal scheduling problem using Artificial immune system algorithm. Int J Electron Power Energy Syst 54:112–122
16. Niknam T, Abarghooee RA, Roosta A (2012) Reserve constrained dynamic economic dispatch: a new fast self-adaptive modified firely algorithm. IEEE Syst J 6(4):635–646
17. Bouchekara HREH, Abido MA, Boucherma M (2014) Optimal power flow using teaching–learning-based optimization technique. Electr Power Syst Res 114:49–59
18. Ghasemi M, Ghavidel S, Gitizadeh M, Akbari E (2015) An improved teaching–learning-based optimization algorithm using Levy mutation strategy for non-smooth optimal power flow. Int J Electr Power Energy Syst 65:375–384
19. Chen C, Wang F, Zhou B, Chan KW, Cao Y, Tan Y (2015) An interval optimization based day-ahead scheduling scheme for renewable energy management in smart distribution systems. Energy Convers Manag 106:584–596
20. Khare V, Nema S, Baredar P (2016) Solar-wind hybrid renewable energy system: a review. Renew Sustain Energy Rev 58:23–33
21. Izadbakhsh M, Gandomkar M, Rezvani A, Ahmadi A (2015) Short-term resource scheduling of a renewable energy based micro grid. Renew Energy 75:598–606
22. Najibi F, Niknam T (2015) Stochastic scheduling of renewable micro-grids considering photovoltaic source uncertainties. Energy Convers Manag 98:484–499
23. Reddy SS, Momoh JA (2015) Realistic and transparent optimum scheduling strategy for hybrid power system. IEEE Trans Smart Grid 6(6):3114–3125
24. Sahin C, Shahidehpour M, Erkmen I (2013) Allocation of hourly reserve versus demand response for security-constrained scheduling of stochastic wind energy. IEEE Trans Sustain Energy 4(1):219–228
25. Jadhav HT, Chawla D, Roy R (2012) Modified teaching learning based algorithm for economic load dispatch incorporating wind power. In: 11th International conference on environment and electrical engineering (EEEIC), pp 397–402
26. Ortega-Vazquez MA, Kirschen DS (2009) Estimating the spinning reserve requirements in systems with significant wind power generation penetration. IEEE Trans Power Syst 24(1):114–124
27. Ortega-Vazquez MA, Kirschen DS (2009) Should the spinning reserve procurement in systems with wind power generation be deterministic or probabilistic? In: International conference on sustainable power generation and supply, pp 1–9
28. Satapathy SC, Naik A (2011) Data clustering based on teaching learning based optimization. In: Proceedings of 2nd international conference on swarm, evolutionary, and memetic computing, Vol Part II, pp 148–156
29. Hejazi HA, Mohabati HR, Hosseinian SH, Abedi M (2011) Differential evolution algorithm for security-constrained energy and reserve optimization considering credible contingencies. IEEE Trans Power Syst 26:1145–1155
30. Hemamalini S, Simon SP (2008) Emission constrained economic dispatch with valve-point effect using particle swarm optimization. In: IEEE region 10 conference (TENCON), pp 1–6
31. IEA Wind Energy (2009) Annual report 2008 international energy agency
32. [Online]. Available: http://www.uwig.org/windinmarketstableOct2011
33. Hetzer J, Yu DC, Bhattarai K (2008) An economic dispatch model incorporating wind power. IEEE Trans Energy Conver 23:603–611
34. Reddy SS, Bijwe PR, Abhyankar AR (2015) Optimal posturing in day-ahead market clearing for uncertainties considering anticipated real-time adjustment costs. IEEE Syst J 9(1):177–190
35. Reddy SS, Bijwe PR, Abhyankar AR (2015) Joint energy and spinning reserve market clearing incorporating wind power and load forecast uncertainties. IEEE Syst J 9(1):152–164
36. Hazra J, Sinha AK (2007) Congestion management using multi-objective particle swarm optimization. IEEE Trans Power Syst 22(4):1726–1734
37. PJM Manual: Energy and Ancillary Services Market Operations [Online]. Available: http://www.pjm.com
38. Motamedi A, Firuzabad MF, Zareipour H (2008) Optimum simultaneous clearing of energy and spinning reserve markets using cost/benefit analysis. In: IEEE power and energy society general meeting, pp 1–7
39. Fu Y, Shahidehpour M, Li Z (2005) Security-constrained unit commitment with AC constraints. IEEE Trans Power Syst 20(3):1538–1550
40. Surender Reddy S, Panigrahi BK, Kundu R, Mukherjee R, Debchoudhury S (2013) Energy and spinning reserve scheduling for a wind-thermal power system using CMA-ES with mean learning technique. Electr Power Energy Syst 53:113–122
41. Arroyo JM, Galiana FD (2005) Energy and reserve pricing in security and network -constrained electricity markets. IEEE Trans Power Syst 20(2):634–643
42. Rao R, Savsani VJ, Vakharia D (2011) Teachinglearning-based optimization: a novel method for constrained mechanical design optimization problems. IEEE Trans Comput Aided Des 43(3):303–315
43. Crepinek M, Liu SH, Mernik L (2012) A note on teachinglearning-based optimization algorithm. Inf Sci 212:79–93
44. Rao R, Savsani VJ, Vakharia D (2012) Teachinglearning-based optimization: an optimization method for continuous non-linear large scale problems. Inf Sci 183(1):1–15
45. Surender Reddy S, Abhyankar AR, Bijwe PR (2012) Market clearing for a wind-thermal power system incorporating wind generation and load forecast uncertainties. In: IEEE PES general meeting, pp 1–8
46. Justus CG, Hargraves WR, Mikhail A, Graber D (1978) Methods for estimating wind speed frequency distributions. J Appl Meteorol 17:350–353
47. Patel MR (1999) Wind and solar power systems. CRC Press, Raton, FL
48. Masters GM (2004) Renewable and efficient electric power systems. Wiley, New Jersey
49. Bhuiyan FA, Yazdani A (2010) Reliability assessment of a wind-power system with integrated energy storage. IET Renew Power Gen 4:211–220
50. Liu X, Xu W (2010) Minimum emission dispatch constrained by stochastic wind power availability and cost. IEEE Trans Power Syst 25:1705–1713
51. Roy S (2002) Market constrained optimal planning for wind energy conversion systems over multiple installation sites. IEEE Trans Energy Conver 17:124–129
52. Liu X, Xu W (2010) Economic load dispatch constrained by wind power availability: a here-and-now approach. IEEE Trans Sustain Energy 1:2–9
53. [Online]. Available: http://www.ee.washington.edu/research/pstca