Abstract
The purpose of this chapter is investigating the unit commitment problem (UCP) in the presence of renewable energy sources (RESs), energy storage systems (ESSs), and modeling the uncertainties arising in this regard. To achieve this goal, the following subjects are presented in detail. The classic UC formulations, the uncertainties’ impacts on this problem, and the new research efforts in this regard are addressed. Also, the existing optimization methods applied to solve the UCP such as robust optimization (RO), information gap decision theory (IGDT), and Taguchi orthogonal array technique (TOAT) as well as their advantages and drawbacks are described in the next section. Also, the application techniques for modeling the renewable energy sources and energy storage systems are detailed. Various models of UC problem such as thermal power plants and thermal power plants combined with RESs and ESSs considering the most important uncertainties in the inactive networks are presented. The proposed models have been tested on standard case of IEEE, 10 units, and the results are presented.
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References
Shahbazitabar, M., & Abdi, H. (2017). A solution to the unit commitment problem applying a hierarchical combination algorithm. Journal of Energy Management and Technology, 1(2), 12–19.
Saravanan, B., et al. (2013). A solution to the unit commitment problem—A review. Frontiers in Energy, 7(2), 223–236.
Tahanan, M., et al., (2014). Large-scale unit commitment under uncertainty: A literature survey. Pisa, IT: UniversitĂ di Pisa.
Abdi, H., Moradi, A., & Saleh, S. (2015). Optimal unit commitment of renewable energy sources in the micro-grids with storage devices. Journal of Intelligent & Fuzzy Systems, 28(2), 537–546.
Zheng, Q. P., Wang, J., & Liu, A. L. (2015). Stochastic optimization for unit commitment—A review. IEEE Transactions on Power Systems, 30(4), 1913–1924.
Shi, J., & Oren, S. S. (2018). Stochastic unit commitment with topology control recourse for power systems with large-scale renewable integration. IEEE Transactions on Power Systems, 33(3), 3315–3324.
Blanco, I., & Morales, J. M. (2017). An efficient robust solution to the two-stage stochastic unit commitment problem. IEEE Transactions on Power Systems, 32(6), 4477–4488.
Bakirtzis, E. A., et al. (2018). Storage management by rolling stochastic unit commitment for high renewable energy penetration. Electric Power Systems Research, 158, 240–249.
Pozo, D., Contreras, J., & Sauma, E. E. (2014). Unit commitment with ideal and generic energy storage units. IEEE Transactions on Power Systems, 29(6), 2974–2984.
Alizadeh, M. I., Moghaddam, M. P., & Amjady, N. (2018). Multistage multiresolution robust unit commitment with nondeterministic flexible ramp considering load and wind variabilities. IEEE Transactions on Sustainable Energy, 9(2), 872–883.
Zhou, M., et al. (2018). Multi-objective unit commitment under hybrid uncertainties: A data-driven approach. In Networking, Sensing and Control (ICNSC), 2018 IEEE 15th International Conference on, IEEE.
Aghaei, J., et al. (2017). Optimal robust unit commitment of CHP plants in electricity markets using information gap decision theory. IEEE Transactions on Smart Grid, 8(5), 2296–2304.
Jiang, R., Wang, J., & Guan, Y. (2012). Robust unit commitment with wind power and pumped storage hydro. IEEE Transactions on Power Systems, 27(2), 800.
Zhao, C., et al. (2013). Multi-stage robust unit commitment considering wind and demand response uncertainties. IEEE Transactions on Power Systems, 28(3), 2708–2717.
Abujarad, S. Y., Mustafa, M., & Jamian, J. (2017). Recent approaches of unit commitment in the presence of intermittent renewable energy resources: A review. Renewable and Sustainable Energy Reviews, 70, 215–223.
Hosseini, S. H., Khodaei, A., & Aminifar, F. (2007). A novel straightforward unit commitment method for large-scale power systems. IEEE Transactions on Power Systems, 22(4), 2134–2143.
Shahbazitabar, M., & Abdi, H. (2018). A novel priority-based stochastic unit commitment considering renewable energy sources and parking lot cooperation. Energy. https://doi.org/10.1016/j.energy.2018.07.025.
Othman, M., et al. (2015). Solving unit commitment problem using multi-agent evolutionary programming incorporating priority list. Arabian Journal for Science and Engineering, 40(11), 3247–3261.
Sen, S., & Kothari, D. P. (1998). Optimal thermal generating unit commitment: A review. Electrical Power & Energy Systems, 20(7), 443–451.
Sheble, G. B., & Fahd, G. N. (1994). Unit commitment literature synopsis. IEEE Transactions on Power Systems, 9(1), 128–135.
Saber, A., et al. (2007). Unit commitment computation by fuzzy adaptive particle swarm optimisation. IET Generation, Transmission & Distribution, 1(3), 456–465.
Chandrasekaran, K., et al. (2012). Thermal unit commitment using binary/real coded artificial bee colony algorithm. Electric Power Systems Research, 84(1), 109–119.
Dieu, V. N., & Ongsakul, W. (2008). Ramp rate constrained unit commitment by improved priority list and augmented Lagrange Hopfield network. Electric Power Systems Research, 78(3), 291–301.
Zhu, J. (2015). Optimization of power system operation (Vol. 47). New Jersey, USA: John Wiley & Sons, IEEE Press.
Soroudi, A., & Amraee, T. (2013). Decision making under uncertainty in energy systems: State of the art. Renewable and Sustainable Energy Reviews, 28, 376–384.
Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine Learning, 3(2), 95–99.
Ben-Haim, Y. (2006). Info-gap decision theory: Decisions under severe uncertainty. Great Britain: Elsevier.
Hong, Y.-Y., Lin, F.-J., & Yu, T.-H. (2016). Taguchi method-based probabilistic load flow studies considering uncertain renewables and loads. IET Renewable Power Generation, 10(2), 221–227.
Kazarlis, S. A., Bakirtzis, A., & Petridis, V. (1996). A genetic algorithm solution to the unit commitment problem. IEEE Transactions on Power Systems, 11(1), 83–92.
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Nikzad, H.R., Abdi, H., Abbasi, S. (2019). Robust Unit Commitment Applying Information Gap Decision Theory and Taguchi Orthogonal Array Technique. In: Mohammadi-ivatloo, B., Nazari-Heris, M. (eds) Robust Optimal Planning and Operation of Electrical Energy Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-04296-7_7
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