Optimal Scheduled Unit Commitment Considering Wind Uncertainty Using Cuckoo Search Algorithm
محورهای موضوعی : Stochastics ProblemsSaniya Maghsudlu 1 , Sirus Mohammadi 2
1 - Department of Electrical Engineering, Bandar Abbas Branch, Islamic Azad University, Bandar Abbas, Iran
2 - Department Of Electrical Engineering, Gachsaran Branch, Islamic Azad University, Gachsaran, Iran
کلید واژه: Monte Carlo Simulation, Wind power, Renewable Energy, Cuckoo search algorithm,
چکیده مقاله :
In this paper, a new method to review the role of wind units as an energy-producer in the scheduling problem of unit commitment is presented. Today, renewable energy sources due to lack of environmental pollution, absence of dependence on fossil fuels, and consequently a very low marginal cost, have been receiving considerable attention in power system. But these sources are associated with uncertainty, solving unit commitment problem as a traditional power system optimization program that attempts to determine optimal entry and exit units and optimal production per unit minimizes the total cost of production. Then, in this study using an iterative algorithm randomly with allocation of probability density functions fits the wind speed, Uncertainty of production wind units has been modeled in the unit commitment program. Economic Analysis of UC with wind power is performed in order to minimize total system cost. In this paper to achieve the optimum solution, a meta-heuristic Cuckoo search (CS) algorithm with high convergence speed is used to solve the unit commitment problem considering IEEE standard 10 unit test system. The simulations results show the effectiveness of the proposed method for reducing production costs and improving load profiles.
In this paper, a new method to review the role of wind units as an energy-producer in the scheduling problem of unit commitment is presented. Today, renewable energy sources due to lack of environmental pollution, absence of dependence on fossil fuels, and consequently a very low marginal cost, have been receiving considerable attention in power system. But these sources are associated with uncertainty, solving unit commitment problem as a traditional power system optimization program that attempts to determine optimal entry and exit units and optimal production per unit minimizes the total cost of production. Then, in this study using an iterative algorithm randomly with allocation of probability density functions fits the wind speed, Uncertainty of production wind units has been modeled in the unit commitment program. Economic Analysis of UC with wind power is performed in order to minimize total system cost. In this paper to achieve the optimum solution, a meta-heuristic Cuckoo search (CS) algorithm with high convergence speed is used to solve the unit commitment problem considering IEEE standard 10 unit test system. The simulations results show the effectiveness of the proposed method for reducing production costs and improving load profiles.
Aoife, M., Foley, G., Leahy, A., & Eamon, J. (2012)."Current methods and advances in forecasting of wind power generation." Renewable Energy, 37, 1-8.
Beurskens, L.W.M., & Hekkenberg, M. (2010). "Renewable Energy Projections as Published in the National Renewable Energy Action Plans of the European Member St ates. ECN Policy Studies", ECN-E,10-069.
Carrion, M., & Arroyo, J.M. (2006)."A Computationally efficient mixed-integer linear formulation for the thermal unit commitment problem". IEEE Trans. Power Syst ,21(3),1371-1378.
Chandrasekaran, K., Hemamalini, S., Sishaj, P., & Simon, N. (2012)."Thermal unit commitment using binary/real coded artificial bee colony algorithm". Electric Power Systems,84,109– 119.
Chen, C.L. (2008)." Optimal wind–thermal generating unit commitment". IEEE Trans Energy Convers, 23,273–80.
Ebrahimi, J., Hosseinian, S. H., & Gharehpetian, G.B. (2011)."Unit commitment problem solution using shuffled frog leaping algorithm". IEEE Trans. Power Syst, 26(2), 573-581.
Enayati, M., & Mohammadi, S. (2015). Reducing Total Harmonic Content of 7-Level Inverter by Use of Cuckoo Algorithm. International Journal of Electronics Communication and Computer Engineering, 6(1), 2278–4209
Ernst, B., Oakleaf, B., Ahlstrom, M., Lange, M., Moehrlen, M., Lange, B., Focken, U., & Rohrig, K. (2007). "Predicting the wind" .IEEE Power Energy Mag., 5(6), 78–89.
Govardhan, M., & Roy, R. (2015). Economic analysis of unit commitment with distributed energy resources". Eiecterical Power and Energy Systems,71,1-14.
Jager, D., & Andreas, A. (1996). NREL National Wind Technology Center (NWTC): M2 Tower; Boulder, Colorado (Data); NREL Report No. DA-5500-56489. http://dx.doi.org/10.5439/1052222.
Jeong, Y.W., Park, J.B., Jang S. H., & Lee K.Y. (2010). "A new quantum-inspired binary PSO: application to unit commitment problems for power system" .IEEE Trans. power syst,25(3) 1486-1495.
Jiang, R., Wang, J., & Guan, Y. (2012). "Robust unit commitment with wind power and pumped storage hydro".IEEE Trans. Power Syst,27(2), 800–810.
José, F., Restrepo, F., & Galiana, D. (2011). "Assessing the Yearly Impact of Wind Power Through a New Hybrid Deterministic/Stochastic Unit Commitment" .IEEE Transactions On Power Systems,26(1), 401-410.
Kerr, R.H., Scheidt, J.L., Fontana, A.J., &Wiley, J.k. (1966)."Unit commitment". IEEE Trans. Power App, 85(5), 417-421.
Kiviluoma, J., O’Malley, M., Tuohy, A., Meibom, P., Milligan, M., Lange, M., Holttinen, M., & Gibescu, M. (2011)."Impact of Wind Power on the Unit Commitment, Operating Reserves, and Market Design". IEEE International Conference Power & Energy society General Meeting.
Li, T., & Shahidehpour, M. (2005)."Price-based unit commitment: a case of Langrangian relaxation versus mixed integer programming". IEEE Trans. Power Syst,20(4),2015-2025.
Liu,Z., et al. (2011)." Optimal Siting and Sizing of Distributed Generators in Distribution Systems Considering Uncertainties". IEEE Transactions on Power Delivery, 26, 2541-2551.
Moghimi Hadji, M., & Vahidi,B.( 2012)."Solution to the Unit Commitment Problem Using Imperialistic Competition Algorithm". IEEE Transactions on Power Systems,27(1), 117-124.
Mohammadi, S., Mozafari, B., Solymani, S., & Niknam, T. (2014)." Stochastic scenario-based model and investigating size of energy storages for PEM-fuel cell unit commitment of micro-grid considering profitable strategies". IET Gene, Transm & Distrib, 8(7),1228–1243.
Pozo, D., & Contreras, J. (2013)."A Chance-Constrained Unit Commitment With an n-k Security Criterion and Significant Wind Generation". IEEE Transactions on Power Systems, 28(3), 2842 – 2851.
Rajabioun, R. (2011)." Cuckoo Optimization Algorithm". Applied Soft Computing ,11 ,5508–5518.
Rathore, CH., & Roy, R. (2016)."Impact of wind uncertainty, plug-in-electric vehicles and demand response program on transmission network expansion planning". Electrical Power and Energy Systems ,75 ,59–73.
Sideratos, G., & Hatziargyriou, N. (2007). "An advanced statistical method for wind power forecasting". IEEE Trans. Power Syst, 22(1),258–26.
Taylor, J., McSharry, P., & Buizza, R. (2009). "Wind power density forecasting using ensemble predictions and time series models" .IEEE Trans. Energy Convers,24(3), 775–782.
Soleymani, S., Mosayebian, M., & Mohammadi, S.(2015). "A combination method for modeling wind power plants in power systems reliability evaluation".Computers and Electrical Engineering, 41, 28-39.
Swarup, K. S., & Yamashiro, S. (2002)."Unit commitment solution methodology using genetic algorithm". IEEE Trans power syst,17(1), 87-91.
Talebizadeh, E., Rashidinejad, M., & Abdollahi, A. (2014). "Evaluation of plug-in electric vehicles impact on cost-based unit Commitment".Journal of Power Sources,248 ,545-552.
Hetzer ,J., Yu, D., & Bhattarai ,K. (2008). "An economic dispatch model incorporating wind power". IEEE Trans Energy Convers,23,603–11.
Wang, Q., Guan, Y., & Wan, J. (2012). "A chance-constrained two-stage stochastic program for unit commitment with uncertain wind power output" .IEEE Trans. Power Syst, 27(1), 206–215.
wood, A. J., & Wollenberg, B. F. (1996). Power Generation Operation and Control. 2nd Ed, New York, John Wiley and Sons.
Yousefi, A., Iu,H.H.C., Fernado, T., &Trinh ,H .( 2013). "An approach for wind power integration using demand side resources". IEEE Trans Sustain Energy,4,917–24.
Zhang, Y., Yao, F. C., Fernando, T., & Trinh, H. (2015). "Wind–thermal systems operation optimization considering emission problem". Electr Power Energy Syst,65,238–45.
Zhang,H., & Li, P . (2010)."Probabilistic analysis for optimal power flow under uncertainty", IET Gener. Transm. Distrib,4(5), 553–561.
Zhao, C., Wang, J., Watson, J.P., & Guan, Y. (2013). "Multi-stage robust unit commitment considering wind and demand response uncertainties". IEEE Trans Power Syst,28(3), 2708–2717.
