Optimal Unit Commitment Problem Considering Stochastic Wind Energy Penetration
Wind energy has attracted much attention as a clean energy resource with low running cost over the last decade,. However, due to the unpredictable nature of wind speed, the Unit Commitment (UC) problem including wind power becomes more difficult. Therefore, engineers and researchers are required to seek reliable models and techniques to plan the operation of thermal units in presence of wind farms. This paper presents a new attempt to solve the stochastic UC including wind energy sources. In order to achieve this, the problem is modeled as a chance-constrained optimization problem. Then, a method based on the here-and-now strategy is used to convert the uncertain power balance constraint into a deterministic constraint. The obtained deterministic problem is modeled using Mixed Integer Programming (MIP) on GAMS interface whereas the CEPLEX MIP solver is employed for its solution.
Keywords:stochastic optimization, mixed-integer programming, unit commitment, wind energy sources
F. H. Aghdam and M. T. Hagh, "Security Constrained Unit Commitment (SCUC) formulation and its solving with Modified Imperialist Competitive Algorithm (MICA," Journal of King Saud University - Engineering Sciences, vol. 31, no. 3, pp. 253-261, Jul. 2019. DOI: https://doi.org/10.1016/j.jksues.2017.08.003
J. Alemany, L. Kasprzyk, and F. Magnago, "Effects of binary variables in mixed integer linear programming based unit commitment in large-scale electricity markets," Electric Power Systems Research, vol. 160, pp. 429-438, Jul. 2018. DOI: https://doi.org/10.1016/j.epsr.2018.03.019
J. Alemany and F. Magnago, "Benders decomposition applied to security constrained unit commitment: Initialization of the algorithm," International Journal of Electrical Power & Energy Systems, vol. 66, pp. 53-66, Mar. 2015. DOI: https://doi.org/10.1016/j.ijepes.2014.10.044
R. Anand, D. Aggarwal, and V. Kumar, "A comparative analysis of optimization solvers," Journal of Statistics and Management Systems, vol. 20, no. 4, pp. 623-635, Jul. 2017. DOI: https://doi.org/10.1080/09720510.2017.1395182
S. Atakan, G. Lulli, and S. Sen, "A State Transition MIP Formulation for the Unit Commitment Problem," IEEE Transactions on Power Systems, vol. 33, no. 1, pp. 736-748, Jan. 2018. DOI: https://doi.org/10.1109/TPWRS.2017.2695964
F. Barani, M. Mirhosseini, H. Nezamabadi-pour, and M. M. Farsangi, "Unit commitment by an improved binary quantum GSA," Applied Soft Computing, vol. 60, pp. 180-189, Nov. 2017. DOI: https://doi.org/10.1016/j.asoc.2017.06.051
M. Carrion and J. M. Arroyo, "A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem," IEEE Transactions on Power Systems, vol. 21, no. 3, pp. 1371-1378, Aug. 2006. DOI: https://doi.org/10.1109/TPWRS.2006.876672
R. Dai, Q. Qi, and J. D. McCalley, "Stochastic unit commitment for wind power interconnected system reserve requirement estimation," in IEEE Power Energy Society General Meeting, Chicago, IL, USA, Jul. 2017, pp. 1-5,. DOI: https://doi.org/10.1109/PESGM.2017.8274483
I. G. Damousis, A. G. Bakirtzis, and P. S. Dokopoulos, "A solution to the unit-commitment problem using integer-coded genetic algorithm," IEEE Transactions on Power Systems, vol. 19, no. 2, pp. 1165-1172, May 2004. DOI: https://doi.org/10.1109/TPWRS.2003.821625
F. Fazelpour, N. Tarashkar, and M. A. Rosen, "Short-term wind speed forecasting using artificial neural networks for Tehran, Iran," International Journal of Energy and Environmental Engineering, vol. 7, no. 4, pp. 377-390, Dec. 2016. DOI: https://doi.org/10.1007/s40095-016-0220-6
U. B. Filik and T. Filik, "Wind Speed Prediction Using Artificial Neural Networks Based on Multiple Local Measurements in Eskisehir," Energy Procedia, vol. 107, pp. 264-269, Feb. 2017. DOI: https://doi.org/10.1016/j.egypro.2016.12.147
A. M. Foley, P. G. Leahy, A. Marvuglia, and E. J. McKeogh, "Current methods and advances in forecasting of wind power generation," Renewable Energy, vol. 37, no. 1, pp. 1-8, Jan. 2012. DOI: https://doi.org/10.1016/j.renene.2011.05.033
A. A. Girgis and S. Varadan, "Unit commitment using load forecasting based on artificial neural networks," Electric Power Systems Research, vol. 32, no. 3, pp. 213-217, Mar. 1995. DOI: https://doi.org/10.1016/0378-7796(94)00917-S
M. Haberg, "Fundamentals and recent developments in stochastic unit commitment," International Journal of Electrical Power & Energy Systems, vol. 109, pp. 38-48, Jul. 2019. DOI: https://doi.org/10.1016/j.ijepes.2019.01.037
G. Haddadian, N. Khalili, M. Khodayar, and M. Shahidehpour, "Optimal coordination of variable renewable resources and electric vehicles as distributed storage for energy sustainability," Sustainable Energy, Grids and Networks, vol. 6, pp. 14-24, Jun. 2016. DOI: https://doi.org/10.1016/j.segan.2015.12.001
M. Hayashi and K. Nagasaka, "Wind speed prediction and determination of wind power output with multi-area weather data by deterministic chaos," in International Conference on Advanced Mechatronic Systems, Kumamoto, Japan, Aug. 2014, pp. 192-197,. DOI: https://doi.org/10.1109/ICAMechS.2014.6911649
J. Hetzer, D. C. Yu, and K. Bhattarai, "An Economic Dispatch Model Incorporating Wind Power," IEEE Transactions on Energy Conversion, vol. 23, no. 2, pp. 603-611, Jun. 2008. DOI: https://doi.org/10.1109/TEC.2007.914171
M. Z. Jahromi, M. M. H. Bioki, M. Rashidinejad, and R. Fadaeinedjad, "Solution to the unit commitment problem using an artificial neural network," Turkish Journal of Electrical Engineering & Computer Sciences, vol. 21, pp. 198-212, 2013.
K.-H. Jo and M.-K. Kim, "Stochastic Unit Commitment Based on Multi-Scenario Tree Method Considering Uncertainty," Energies, vol. 11, no. 4, Apr. 2018. DOI: https://doi.org/10.3390/en11040740
R. C. Johnson, H. H. Happ, and W. J. Wright, "Large Scale Hydro-Thermal Unit Commitment-Method and Results," IEEE Transactions on Power Apparatus and Systems, vol. 90, no. 3, pp. 1373-1384, May 1971. DOI: https://doi.org/10.1109/TPAS.1971.292941
P. Jong, A. Kiperstok, and E. A. Torres, "Economic and environmental analysis of electricity generation technologies in Brazil," Renewable and Sustainable Energy Reviews, vol. 52, pp. 725-739, Dec. 2015. DOI: https://doi.org/10.1016/j.rser.2015.06.064
R. Jursa and K. Rohrig, "Short-term wind power forecasting using evolutionary algorithms for the automated specification of artificial intelligence models," International Journal of Forecasting, vol. 24, no. 4, pp. 694-709, Oct. 2008. DOI: https://doi.org/10.1016/j.ijforecast.2008.08.007
S. R. K, L. Panwar, B. K. Panigrahi, and R. Kumar, "Binary whale optimization algorithm: a new metaheuristic approach for profit-based unit commitment problems in competitive electricity markets," Engineering Optimization, vol. 51, no. 3, pp. 369-389, Mar. 2019. DOI: https://doi.org/10.1080/0305215X.2018.1463527
L. Landberg, "A mathematical look at a physical power prediction model," Wind Energy, vol. 1, no. 1, pp. 23-28, 1998. DOI: https://doi.org/10.1002/(SICI)1099-1824(199809)1:1<23::AID-WE9>3.0.CO;2-9
H. Liu, H.-Q. Tian, C. Chen, and Y. Li, "A hybrid statistical method to predict wind speed and wind power," Renewable Energy, vol. 35, no. 8, pp. 1857-1861, Aug. 2010. DOI: https://doi.org/10.1016/j.renene.2009.12.011
X. Liu and W. Xu, "Economic Load Dispatch Constrained by Wind Power Availability: A Here-and-Now Approach," IEEE Transactions on Sustainable Energy, vol. 1, no. 1, pp. 2-9, Apr. 2010. DOI: https://doi.org/10.1109/TSTE.2010.2044817
H. Ma and S. M. Shahidehpour, "Transmission-constrained unit commitment based on Benders decomposition," International Journal of Electrical Power & Energy Systems, vol. 20, no. 4, pp. 287-294, May 1998. DOI: https://doi.org/10.1016/S0142-0615(97)00058-6
Ι. Marouani, A. Boudjemline, T. Guesmi, and H. H. Abdallah, "A Modified Artificial Bee Colony for the Non-Smooth Dynamic Economic/Environmental Dispatch," Engineering, Technology & Applied Science Research, vol. 8, no. 5, pp. 3321-3328, Oct. 2018. DOI: https://doi.org/10.48084/etasr.2098
G. Morales-Espana, C. Gentile, and A. Ramos, "Tight MIP formulations of the power-based unit commitment problem," OR Spectrum, vol. 37, no. 4, pp. 929-950, Oct. 2015. DOI: https://doi.org/10.1007/s00291-015-0400-4
G. Morales-Espana, A. Lorca, and M. M. Weerdt, "Robust unit commitment with dispatchable wind power," Electric Power Systems Research, vol. 155, pp. 58-66, Feb. 2018. DOI: https://doi.org/10.1016/j.epsr.2017.10.002
D. Murata and S. Yamashiro, "Unit commitment scheduling by Lagrange relaxation method taking into account transmission losses," Electrical Engineering in Japan, vol. 152, no. 4, pp. 27-33, 2005. DOI: https://doi.org/10.1002/eej.20119
M. Nemati, M. Braun, and S. Tenbohlen, "Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming," Applied Energy, vol. 210, pp. 944-963, Jan. 2018. DOI: https://doi.org/10.1016/j.apenergy.2017.07.007
J. Ostrowski, M. F. Anjos, and A. Vannelli, "Tight Mixed Integer Linear Programming Formulations for the Unit Commitment Problem," IEEE Transactions on Power Systems, vol. 27, no. 1, pp. 39-46, Feb. 2012. DOI: https://doi.org/10.1109/TPWRS.2011.2162008
S. K. Paramasivan and D. Lopez, "Forecasting of Wind Speed using Feature Selection and Neural Networks," International Journal of Renewable Energy Research, vol. 6, no. 3, pp. 833-837, Sep. 2016.
M. R. Patel, Wind and Solar Power Systems: design, analysis, and operation, Second Edition. London, UK: Taylor & Francis Group, 2006. DOI: https://doi.org/10.1201/9781420039924
S. M. Pour, J. H. Drake, L. S. Ejlertsen, K. M. Rasmussen, and E. K. Burke, "A hybrid Constraint Programming/Mixed Integer Programming framework for the preventive signaling maintenance crew scheduling problem," European Journal of Operational Research, vol. 269, no. 1, pp. 341-352, Aug. 2018. DOI: https://doi.org/10.1016/j.ejor.2017.08.033
D. F. Rahman, A. Viana, and J. P. Pedroso, "Metaheuristic search based methods for unit commitment," International Journal of Electrical Power & Energy Systems, vol. 59, pp. 14-22, Jul. 2014. DOI: https://doi.org/10.1016/j.ijepes.2014.01.038
L. A. C. Roque, D. B. M. M. Fontes, and F. A. C. C. Fontes, "A multi-objective unit commitment problem combining economic and environmental criteria in a metaheuristic approach," Energy Procedia, vol. 136, pp. 362-368, Oct. 2017. DOI: https://doi.org/10.1016/j.egypro.2017.10.290
B. Saravanan, E. R. Vasudevan, and D. P. Kothari, "Unit commitment problem solution using invasive weed optimization algorithm," International Journal of Electrical Power & Energy Systems, vol. 55, pp. 21-28, Feb. 2014. DOI: https://doi.org/10.1016/j.ijepes.2013.08.020
G. Sideratos and N. D. Hatziargyriou, "An Advanced Statistical Method for Wind Power Forecasting," IEEE Transactions on Power Systems, vol. 22, no. 1, pp. 258-265, Feb. 2007. DOI: https://doi.org/10.1109/TPWRS.2006.889078
J. Sillmann, "Understanding, modeling and predicting weather and climate extremes: Challenges and opportunities," Weather and Climate Extremes, vol. 18, pp. 65-74, Dec. 2017. DOI: https://doi.org/10.1016/j.wace.2017.10.003
W. L. Snyder, H. D. Powell, and J. C. Rayburn, "Dynamic Programming Approach to Unit Commitment," IEEE Transactions on Power Systems, vol. 2, no. 2, pp. 339-348, May 1987. DOI: https://doi.org/10.1109/TPWRS.1987.4335130
A. Torchani, A. Boudjemline, H. Gasmi, Y. Bouazzi, and T. Guesmi, "Dynamic Economic/Environmental Dispatch Problem Considering Prohibited Operating Zones," Engineering, Technology & Applied Science Research, vol. 9, no. 5, pp. 4586-4590, Oct. 2019. DOI: https://doi.org/10.48084/etasr.2904
V. K. Tumuluru, Z. Huang, and D. H. K. Tsang, "Unit commitment problem: A new formulation and solution method," International Journal of Electrical Power & Energy Systems, vol. 57, pp. 222-231, May 2014. DOI: https://doi.org/10.1016/j.ijepes.2013.11.043
S. Virmani, E. C. Adrian, K. Imhof, and S. Mukherjee, "Implementation of a Lagrangian relaxation based unit commitment problem," IEEE Transactions on Power Systems, vol. 4, no. 4, pp. 1373-1380, Nov. 1989. DOI: https://doi.org/10.1109/59.41687
B. Zhu, M. Chen, N. Wade, and L. Ran, "A prediction model for wind farm power generation based on fuzzy modeling," Procedia Environmental Sciences, vol. 12, pp. 122-129, Jan. 2012. DOI: https://doi.org/10.1016/j.proenv.2012.01.256
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