A solution to stochastic unit commitment problem for a wind-thermal system coordination

Abstract

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.

Key words： unit commitment (UC); randomness; wind generation; univariate; chance constrained

References

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

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

4 OuyangZ, ShahidehpourS M. An intelligent dynamic programming for unit commitment application. IEEE Transactions on Power Systems, 1991, 6(3): 1203–1209

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

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

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

9 EslamianM, HosseinianS H, VahidiB. Bacterial foraging-based solution to the unit-commitment problem. IEEE Transactions on Power Systems, 2009, 24(3): 1478–1488

10 LeeT Y. Optimal spinning reserve for a wind-thermal power system using EIPSO. IEEE Transactions on Power Systems, 2007, 22(4): 1612–1621

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

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

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

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