© The Institution of Engineering and Technology
Wind energy is intermittent and uncertain. This uncertainty creates additional risk in the day-ahead 24-h dispatch schedule. Wind speed can be forecasted for the next 24-h and hourly power forecasts can be best described using probabilistic models. Security and risk constrained probabilistic unit commitment (SRCPUC) algorithms considering probabilistic forecast models of wind power can be used to optimally schedule conventional and wind generation to minimise the total cost and minimise risk. However, inclusion of non-linear probabilistic forecast models in a SRCPUC algorithm is computationally very challenging. In this study, the proposed SRCPUC algorithm uses a triangular approximate distribution (TAD) model to probabilistically represent power output of wind generator. The TAD model quantifies hourly potential risk because of expected energy not served (EENS) from uncertain wind power. Reserves are optimally scheduled to counter EENS. Total energy cost, reserve cost and risk from EENS are minimised in the proposed SRCPUC algorithm. The proposed algorithm is implemented on 6-bus and 118-bus IEEE systems. The results are compared with classical enumeration technique. Significant benefits in computing time (more than 500 times faster) are seen while the numerical results are observed to be highly accurate.
References
-
-
1)
-
9. Wang, J., Botterud, A., Bessa, R., et al: ‘Wind power forecasting uncertainty and unit commitment’, Appl. Energy, 2011, 88, (11), pp. 4014–4023 (doi: 10.1016/j.apenergy.2011.04.011).
-
2)
-
3)
-
Y. Fu ,
M. Shahidehpour ,
Z. Li
.
Security-constrained unit commitment with AC constraints.
IEEE Trans. Power Syst.
,
1538 -
1550
-
4)
-
A. Dukpa ,
I. Dugga ,
B. Venkatesh ,
L. Chang
.
Optimal participation and risk mitigation of wind generators in an electricity market.
IET Renew. Power Gener.
,
2 ,
165 -
175
-
5)
-
6)
-
18. Zhao, C., Guan, Y.: ‘Unified stochastic and robust unit commitment’, IEEE Trans. Power Syst., 2013, 28, (3), pp. 3353–3361 (doi: 10.1109/TPWRS.2013.2251916).
-
7)
-
4. Choling, D., Yu, P., Venkatesh, B.: ‘Effects of security constraints on unit commitment with wind generators’. Power & Energy Society General Meeting, 2009 (PES '09), 2009, pp. 1–6.
-
8)
-
8. Siahkali, H., Vakilian, M.: ‘Stochastic unit commitment of wind farms integrated in power system’, Electr. Power Syst. Res., 2010, 80, (9), pp. 1006–1017 (doi: 10.1016/j.epsr.2010.01.003).
-
9)
-
16. Zhao, C., Wang, J., Watson, J.-P., et al: ‘Multi-stage robust unit commitment considering wind and demand response uncertainties’, IEEE Trans. Power Syst., 2013, 28, (3), pp. 2708–2717 (doi: 10.1109/TPWRS.2013.2244231).
-
10)
-
13. Hodge, B.M., Milligan, M.: ‘Wind power forecasting error distributions over multiple timescales’. IEEE Power and Energy Society General Meeting, Detroit, MI, USA, 2011.
-
11)
-
11. Yu, P., Venkatesh, B.: ‘Triangular approximate distribution model of wind electric generators’. 10th Int. Power and Energy Conf., December 2012.
-
12)
-
10. Yu, P., Venkatesh, B.: ‘A practical real-time OPF method using new triangular approximate model of wind electric generators’, IEEE Trans. Power Syst., 2012, 27, (4), pp. 2036–2046 (doi: 10.1109/TPWRS.2012.2187345).
-
13)
-
14. Zhang, Z.S., Sun, Y.Z., Gao, D.W., et al: ‘A versatile probabilistic distribution model for wind power forecast errors and its application in economic dispatch’, IEEE Trans. Power Syst., 2013, 28, (3), pp. 3114–3125 (doi: 10.1109/TPWRS.2013.2249596).
-
14)
-
S. Kamalinia ,
M. Shahidehpour
.
Generation expansion planning in wind-thermal power systems.
IET Gener. Transm. Distrib.
,
8 ,
940 -
951
-
15)
-
B. Venkatesh ,
P. Yu ,
H.B. Gooi ,
D. Choling
.
Fuzzy MILP unit commitment incorporating wind generators.
IEEE Trans. Power Syst.
,
4 ,
1738 -
1746
-
16)
-
3. Foley, A.M., Leahy, P.G., Marvuglia, A., et al: ‘Current methods and advances in forecasting of wind power generation’, Renew. Energy, 2012, 37, (1), pp. 1–8 (doi: 10.1016/j.renene.2011.05.033).
-
17)
-
A. Fabbri ,
T.G.S. Román ,
J.R. Abbad ,
V.H.M. Quezada
.
Assessment of the cost associated with wind generation prediction errors in a liberalized electricity market.
IEEE Trans. Power Syst.
,
3 ,
1440 -
1446
-
18)
-
7. Yan, Y., Yang, S., Wen, F., MacGill, I.: ‘Generation scheduling with volatile wind power generation’. 2009 Int. Conf. on Sustainable Power Generation and Supply, April 2009, pp. 1–7.
-
19)
-
15. Jiang, R., Wang, J., Zhang, M., et al: ‘Two-stage minimax regret robust unit commitment’, IEEE Trans. Power Syst., 2013, 28, (3), pp. 2271–2282 (doi: 10.1109/TPWRS.2013.2250530).
-
20)
-
2. Schlueter, R.A., Park, G.L., Reddoch, T.W., et al: ‘A modified unit commitment and generation control for utilities with large wind generation penetrations’, Wind Energy, 1985, 104, (7), pp. 1630–1636.
-
21)
-
29. Khodayar, M.E., Shahidehpour, M., Wu, L.: ‘Enhancing the dispatchability of variable wind generation by coordination with pumped-storage hydro units in stochastic power systems’, IEEE Trans. Power Syst., 2013, 28, (3), pp. 2808–2818 (doi: 10.1109/TPWRS.2013.2242099).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2013.0766
Related content
content/journals/10.1049/iet-gtd.2013.0766
pub_keyword,iet_inspecKeyword,pub_concept
6
6