A solution to stochastic unit commitment problem for a wind-thermal system coordination
B. SARAVANAN, Shreya MISHRA, Debrupa NAG
A solution to stochastic unit commitment problem for a wind-thermal system coordination
Unit commitment (UC) problem is one of the most important decision making problems in power system. In this paper the UC problem is solved by considering it as a real time problem by adding stochasticity in the generation side because of wind-thermal co-ordination system as well as stochasticity in the load side by incorporating the randomness of the load. The most important issue that needs to be addressed is the achievement of an economic unit commitment solution after solving UC as a real time problem. This paper proposes a hybrid approach to solve the stochastic unit commitment problem considering the volatile nature of wind and formulating the UC problem as a chance constrained problem in which the load is met with high probability over the entire time period.
unit commitment (UC) / randomness / wind generation / univariate / chance constrained
[1] |
OzturkU A, MazumdarM, NormanB A. A Solution to the stochastic unit commitment problem using chance constrained programming. IEEE Transactions on Power Systems, 2004, 19(3): 1589–1598
CrossRef
Google scholar
|
[2] |
BurnsR M, GibsonC A. Optimization of priority lists for a unit commitment program. In: IEEE/PES Summer Meeting, New York: Institute of Electrical and Electronics Engineers, 1975, Paper A 75 453–1
|
[3] |
SenjyuT, MiyagiT, SaberA Y, UrasakiN, FunabashiT.Emerging solution of large-scale unit commitment problem byStochastic Priority List. Electric Power Systems Research, 2006, 76(5): 283–292
CrossRef
Google scholar
|
[4] |
OuyangZ, ShahidehpourS M. An intelligent dynamic programming for unit commitment application. IEEE Transactions on Power Systems, 1991, 6(3): 1203–1209
CrossRef
Google scholar
|
[5] |
CohenA I, YoshimuraM. A branch-and-bound algorithm for unit commitment. IEEE Transactions on Power Apparatus and Systems, 1983, PAS-102(2): 444–451
CrossRef
Google scholar
|
[6] |
WangY, XiaQ, KangC. A novel security stochastic unit commitment for wind-thermal system operation. In: Proceedings of the 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT). Weihai, China, 2011, 386–393
|
[7] |
ChenC L. Optimal wind-thermal generating unit commitment. IEEE Transactions on Energy Conversion, 2008, 23(1): 273–280
CrossRef
Google scholar
|
[8] |
EbrahimiJ, HosseinianS H, GharehpetianG B. Unit commitment problem solution using shuffled frog leaping algorithm. IEEE Transactions on Power Systems, 2011, 26(2): 573–581
CrossRef
Google scholar
|
[9] |
EslamianM, HosseinianS H, VahidiB. Bacterial foraging-based solution to the unit-commitment problem. IEEE Transactions on Power Systems, 2009, 24(3): 1478–1488
CrossRef
Google scholar
|
[10] |
LeeT Y. Optimal spinning reserve for a wind-thermal power system using EIPSO. IEEE Transactions on Power Systems, 2007, 22(4): 1612–1621
CrossRef
Google scholar
|
[11] |
DaneshiH, SrivastavaA K. Security-constrained unit commitment with wind generation and compressed air energy storage. IET Generation, Transmission & Distribution, 2012, 6(2): 167–175
CrossRef
Google scholar
|
[12] |
WoodJ, WollenbergB F. Power Generation, Operation & Control. John Wiley & Sons, Inc., 1996
|
[13] |
PappalaV S, ErlichI, RohrigK, DobschinskiJ. A stochastic model for the optimal operation of a wind-thermal power system. IEEE Transactions on Power Systems, 2009, 24(2): 940–950
CrossRef
Google scholar
|
[14] |
UmmelsB C, GibescuM, PelgrumE, KlingW L, BrandA J. Impacts of wind power on thermal generation unit commitment and dispatch. IEEE Transactions on Energy Conversion, 2007, 22(1): 44–51
CrossRef
Google scholar
|
[15] |
LogenthiranT, SrinivasanD. Particle swarm optimization for unit commitment Problem. In: 2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). Singapore, 2010, 642–647
|
[16] |
SriyanyongP, SongY H. Unit commitment using particle swarm optimization combined with Lagrange relaxation. In: 2005 IEEE Power Engineering Society General Meeting. San Francisco, USA, 2005, 2752–2759
|
[17] |
HargreavesJ J, HobbsB F. Commitment and dispatch with uncertain wind generation by dynamic programming. IEEE Transactions on sustainable energy, 2012, 3(4): 724–734
|
[18] |
SaravananB, DasS, SikriS, KothariD P. A solution to the unit commitment problem–a review. Frontiers in Energy, 2013, 7(2): 223–236
|
[19] |
SaravananB, SikriS, SwarupK S, KothariD P. Unit commitment using dynamic programming–an exhaustive working of both classical and stochastic approach. Frontiers in Energy, 2013, 7(3): 333–341
|
[20] |
SaravananB, VasudevanE R, KothariD P. A solution to unit commitment problem using invasive weed optimization algorithm. Frontiers in Energy, 2013, 7(4): 487–494
|
/
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