Unit commitment using dynamic programming–an exhaustive working of both classical and stochastic approach
Unit commitment using dynamic programming–an exhaustive working of both classical and stochastic approach
Balasubramaniyan SARAVANAN1(), Surbhi SIKRI1, K. S. SWARUP2, D. P. KOTHARI3
1. School of Electrical Engineering, VIT University, Vellore 632014, India; 2. Department of Electrical Science, IIT Madras, Chennai 600036, India; 3. JB Group of Institutions, Hyderabad, 500075, India
In the present electricity market, where renewable energy power plants have been included in the power systems, there is a lot of unpredictability in the demand and generation. There are many conventional and evolutionary programming techniques used for solving the unit commitment (UC) problem. Dynamic programming (DP) is a conventional algorithm used to solve the deterministic problem. In this paper DP is used to solve the stochastic model of UC problem. The stochastic modeling for load and generation side has been formulated using an approximate state decision approach. The programs were developed in a MATLAB environment and were extensively tested for a four-unit eight-hour system. The results obtained from these techniques were validated with the available literature and outcome was good. The commitment is in such a way that the total cost is minimal. The novelty of this paper lies in the fact that DP is used for solving the stochastic UC problem.
. Unit commitment using dynamic programming–an exhaustive working of both classical and stochastic approach[J]. Frontiers in Energy, 2013, 7(3): 333-341.
Balasubramaniyan SARAVANAN, Surbhi SIKRI, K. S. SWARUP, D. P. KOTHARI. Unit commitment using dynamic programming–an exhaustive working of both classical and stochastic approach. Front Energ, 2013, 7(3): 333-341.
Wood A J, Wollenberg B F. Power Generation Operation and Control. New York: Wiley & Sons, 2003
2
Catalao J P S, Mariano S J P S, Mendes V M F, Ferreira L A F M. Profit based unit commitment with emission limitation: a multiobjective approach. In: Proceedings of IEEE Lausanne conference on Power Technology . Lausanne Switzerland, 2007, 1417–1422
3
Burns R M, Gibson C A. Optimization of priority lists for a unit commitment program. In: Proceedings of IEEE/PES Summer Meeting . San Francisco, USA, 1975, 453–456
4
Ouyang Z, Shahidehpour S M. An intelligent dynamic programming for unit commitment application. IEEE Transactions on Power Systems , 1991, 6(3): 1203–1209 doi: 10.1109/59.119267
5
Virmani S, Adrian E C, Imhof K, Mukherjee S. Implementation of a Lagrangian relaxation based unit commitment problem. IEEE Transactions on Power Systems , 1989, 4(4): 1373–1380 doi: 10.1109/59.41687
6
Ongsakul W, Petcharaks N. Unit commitment by enhanced adaptive Lagrangian relaxation. IEEE Transactions on Power Systems , 2004, 19(1): 620–628 doi: 10.1109/TPWRS.2003.820707
7
Dillon T S, Edwin K W, Kochs H D, Taud R J. Integer programming approach to the problem of optimal unit commitment with probabilistic reserve determination. IEEE Transactions on Power Apparatus and Systems , 1978, PAS-97(6): 2154–2166 doi: 10.1109/TPAS.1978.354719
8
Daneshi H, Choobbari A L, Shahidehpour S M, Li Z Y. Mixed integer programming method to solve security constrained unit commitment with restricted operating zone limits. In: Proceedings of IEEE International Conference on Electro/Information Technology . Ames, USA, 2008, 187-192
9
Cohen A I, Yoshimura M. A branch-and-bound algorithm for unit commitment. IEEE Transactions on Power Apparatus and Systems , 1983, PAS-102(2): 444–451 doi: 10.1109/TPAS.1983.317714
10
Logenthiran T. Formulation of unit commitment (UC) problems and analysis of available methodologies used for solving the problems. In: Proceedings of IEEE International Conference on Sustainable Energy Technologies . Kandy, Sri Lanka, 2010, 1–6
11
Snyder W L, Powell H D, Rayburn J C. Dynamic programming approach to power system unit commitment. IEEE Transactions on Power Systems , 1987, 2(2): 339–348 doi: 10.1109/TPWRS.1987.4335130
