A fast method for the unit scheduling problem with significant renewable power generation

Energy Conversion and Management

Volumn 94, Issue , 2015, Pages 178-189

  Osorio G J ^a   Lujano Rojas, J M ^a   Matias, J C O ^a   Catalao J P S ^a,b,c  

^a UNIVERSITY OF BEIRA INTERIOR   (Portugal)

^b INESC ID   (Portugal)

^c INSTITUTO SUPERIOR TÉCNICO   (Portugal)

Author keywords

Forecasting error; Mixed integer linear programming; Renewable generation; Stochastic unit commitment

Indexed keywords

ELECTRIC POWER SYSTEMS; FORECASTING; INTEGER PROGRAMMING; INTELLIGENT SYSTEMS; MONTE CARLO METHODS; SOLAR ENERGY; STOCHASTIC PROGRAMMING; STOCHASTIC SYSTEMS; WIND POWER;

FORECASTING ERROR; MIXED INTEGER LINEAR PROGRAMMING; PROPORTIONAL RELATIONSHIPS; RENEWABLE GENERATION; RENEWABLE POWER GENERATION; STATISTICAL CHARACTERISTICS; STOCHASTIC UNIT COMMITMENTS; WIND AND SOLAR POWER;

SCHEDULING;

EID: 84922570320     PISSN: 01968904     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.enconman.2015.01.071     Document Type: Article

References (41)

1. P.J. Luickx, E.D. Delarue, and W.D. D'haeseleer Impact of large amounts of wind power on the operation of an electricity generation system: Belgian case study Renew Sustain Energy Rev 14 7 2010 2019 2028
2. G. Cau, D. Cocco, M. Petrollese, S.K. Kaer, and C. Milan Energy management strategy based on short-term generation scheduling for a renewable microgrid using a hydrogen storage system Energy Convers Manage 87 2014 820 831
3. F.J. Santos-Alamillos, D. Pozo-Vázquez, J.A. Ruiz-Arias, V. Lara-Fanego, and J. Tovar-Pescador A methodology for evaluating the spatial variability of wind energy resources: application to assess the potential contribution of wind energy to baseload power Renew Energy 69 2014 147 156
4. M. Mazidi, A. Zakariazadeh, S. Jadid, and P. Siano Integrated scheduling of renewable generation and demand response programs in a microgrid Energy Convers Manage 86 2014 1118 1127
5. A. Tuohy, P. Meibom, E. Denny, and M. O'Malley Unit commitment for systems with significant wind penetration IEEE Trans Power Syst 24 2 2009 592 601
6. J. Wang, A. Botterud, R. Bessa, H. Keko, L. Carvalho, and D. Issicaba Wind power forecasting uncertainty and unit commitment Appl Energy 88 2011 4014 4023
7. P.A. Ruiz, C.R. Philbrick, E. Zak, K.W. Cheung, and P.W. Sauer Uncertainty management in the unit commitment problem IEEE Trans Power Syst 24 2 2009 642 651
8. X. Ding, W.-J. Lee, W. Jianxue, and L. Liu Studies on stochastic unit commitment formulation with flexible generating units Electr Power Syst Res 80 1 2010 130 141
9. B. Ji, X. Yuan, X. Li, Y. Huang, and W. Li Application of quantum-inspired binary gravitational search algorithm for thermal unit commitment with wind power integration Energy Convers Manage 87 2014 589 598
10. Q. Wang, Y. Guan, and J. Wang A chance-constrained two-stage stochastic program for unit commitment with uncertain wind power output IEEE Trans Power Syst 27 1 2012 206 215
11. J.J. Hargreaves, and B.F. Hobbs Commitment and dispatch with uncertain wind generation by dynamic programming IEEE Trans Sustain Energy 3 4 2012 724 734
12. J. Wang, J. Wang, C. Liu, and J.P. Ruiz Stochastic unit commitment with sub-hourly dispatch constraints Appl Energy 105 2013 418 422
13. C. Zhao, and Y. Guan Unified stochastic and robust unit commitment IEEE Trans Power Syst 28 3 2013 3353 3361
14. R. Jiang, M. Zhang, G. Li, and Y. Guan Two-stage network constrained robust unit commitment problem Eur J Oper Res 234 3 2014 751 762
15. P.B. Luh, Y. Yu, B. Zhang, E. Litvinov, T. Zheng, and F. Zhao Grid integration of intermittent wind generation: a Markovian approach IEEE Trans Smart Grid 5 2 2014 732 741
16. A. Sturt, and G. Strbac Efficient stochastic scheduling for simulation of wind-integrated power systems IEEE Trans Power Syst 27 1 2012 323 334
17. B. Ji, X. Yuan, Z. Chen, and H. Tian Improved gravitational search algorithm for unit commitment considering uncertainty of wind power Energy 67 2014 52 62
18. Y. Yao, C. Meyers, S. Smith, and T. Edmunds Large-scale stochastic optimization for unit commitment and economic dispatch 2012 Lawrence Livermore National Laboratory
19. A. Papavasiliou, S.S. Oren, and B. Rountree Applying high performance computing to transmission-constrained stochastic unit commitment for renewable energy integration IEEE Trans Power Syst PP 99 2014 1 12
20. V.S. Pappala, I. Erlich, K. Rohrig, and J. Dobschinski A stochastic model for the optimal operation of a wind-thermal power system IEEE Trans Power Syst 24 2 2009 940 950
21. J.M. Morales, R. Mínguez, and A.J. Conejo A methodology to generate statistically dependent wind speed scenarios Appl Energy 87 3 2010 843 855
22. K. Suomalainen, C.A. Silva, S.P. Ferrão, and S. Connors Synthetic wind speed scenarios including diurnal effects: implications for wind power dimensioning Energy 37 1 2012 41 50
23. H.V. Haghi, M.T. Bina, and M.A. Golkar Nonlinear modeling of temporal wind power variations IEEE Trans Sustain Energy 4 4 2013 838 847
24. L. Baringo, and A.J. Conejo Correlated wind-power production and electric load scenarios for investment decisions Appl Energy 101 2013 475 482
25. X.-Y. Ma, Y.-Z. Sun, and H.-L. Fang Scenario generation of wind power based on statistical uncertainty and variability IEEE Trans Sustain Energy 4 4 2013 894 904
26. G. Díaz, P.G. Casielles, and J. Coto Simulation of spatially correlated wind power in small geographic areas-Sampling methods and evaluation Int J Electr Power Energy Syst 63 2014 513 522
27. F.A. Farret, and M.G. Simões Integration of alternative sources of energy 2006 Wiley
28. R. Billinton, and R.N. Allan Reliability evaluation of power systems 1996 Plenum New York
29. J. Aghaei, T. Niknam, R. Azizipanah-Abarghooee, and J.M. Arroyo Scenario-based dynamic economic emission dispatch considering load and wind power uncertainties Electr Power Energy Syst 47 2013 351 367
30. E. Delarue, D. Cattrysse, and W. D'haeseleer Enhanced priority list unit commitment method for power systems with a high share of renewables Electr Power Syst Res 105 2013 115 123
31. M. Carrión, and J.M. Arroyo A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem IEEE Trans Power Syst 21 3 2006 1371 1378
32. G. Morales-España, J.M. Latorre, and A. Ramos Tight and compact MILP formulation of start-up and shut-down ramping in unit commitment IEEE Trans Power Syst 28 2 2013 1288 1296
33. X. Yuan, A. Su, H. Nie, Y. Yuan, and L. Wang Application of enhanced discrete evolution approach to unit commitment problem Energy Convers Manage 50 2009 2449 2456
34. X. Yuan, H. Tian, S. Zhang, B. Ji, and Y. Hou Second-order cone programming for solving unit commitment strategy of thermal generators Energy Convers Manage 76 2013 20 25
35. X. Yu, and X. Zhang Unit commitment using Lagrangian relaxation and particle swarm optimization Electr Power Energy Syst 61 2014 510 522
36. P.K. Roy, and R. Sarkar Solution of unit commitment problem using quasi-oppositional teaching learning based algorithm Electr Power Energy Syst 60 2014 96 106
37. P.K. Roy Solution of unit commitment problem using gravitational search algorithm Electr Power Energy Syst 53 2013 85 94
38. G. Dudek Genetic algorithm with binary representation of generating unit start-up and shut-down times for the unit commitment problem Expert Syst Appl 40 2013 6080 6086
39. N. Amjady, and M.R. Ansari Hydrothermal unit commitment with AC constraints by a new solution method based on benders decomposition Energy Convers Manage 65 2013 57 65
40. A. Rong, H. Hakonen, and R. Lahdelma A dynamic regrouping based sequential dynamic programming algorithm for unit commitment of combined heat and power systems Energy Convers Manage 50 4 2009 1108 1115
41. V.N. Dieu, and W. Ongsakul Ramp rate constrained unit commitment by improved priority list and augmented Lagrange Hopfield network Electr Power Syst Res 78 3 2008 291 301