A New Particle Swarm Optimization Based Strategy for the Economic Emission Dispatch Problem Including Wind Energy Sources
Received: 9 June 2021 | Revised: 5 July 2021 | Accepted: 11 July 2021 | Online: 12 October 2021
Corresponding author: T. Guesmi
Abstract
Power dispatch has become an important issue due to the high integration of Wind Power (WP) in power grids. Within this context, this paper presents a new Particle Swarm Optimization (PSO) based strategy for solving the stochastic Economic Emission Dispatch Problem (EEDP). This problem was solved considering several constraints such as power balance, generation limits, and Valve Point Loading Effects (VPLEs). The power balance constraint is described by a chance constraint to consider the impact of WP intermittency on the EEDP solution. In this study, the chance constraint represents the tolerance that the power balance constraint cannot meet. The suggested framework was successfully evaluated on a ten-unit system. The problem was solved for various threshold tolerances to study further the impact of WP penetration.
Keywords:
economic emission dispatch, wind energy, stochastic optimization, particle swarm optimizationDownloads
References
P. D. Chung, "Evaluation of Reactive Power Support Capability of Wind Turbines," Engineering, Technology & Applied Science Research, vol. 10, no. 1, pp. 5211-5216, Feb. 2020. https://doi.org/10.48084/etasr.3260
J. Jin, D. Zhou, P. Zhou, and Z. Miao, "Environmental/economic power dispatch with wind power," Renewable Energy, vol. 71, pp. 234-242, Nov. 2014. https://doi.org/10.1016/j.renene.2014.05.045
R. Dong and S. Wang, "New Optimization Algorithm Inspired by Kernel Tricks for the Economic Emission Dispatch Problem With Valve Point," IEEE Access, vol. 8, pp. 16584-16594, 2020. https://doi.org/10.1109/ACCESS.2020.2965725
M. Basu, "Dynamic economic emission dispatch using nondominated sorting genetic algorithm-II," International Journal of Electrical Power & Energy Systems, vol. 30, no. 2, pp. 140-149, Feb. 2008. https://doi.org/10.1016/j.ijepes.2007.06.009
D. Zou, S. Li, Z. Li, and X. Kong, "A new global particle swarm optimization for the economic emission dispatch with or without transmission losses," Energy Conversion and Management, vol. 139, pp. 45-70, May 2017. https://doi.org/10.1016/j.enconman.2017.02.035
Z. X. Liang and J. D. Glover, "A zoom feature for a dynamic programming solution to economic dispatch including transmission losses," IEEE Transactions on Power Systems, vol. 7, no. 2, pp. 544-550, May 1992. https://doi.org/10.1109/59.141757
G. W. Chang et al., "Experiences with mixed integer linear programming based approaches on short-term hydro scheduling," IEEE Transactions on Power Systems, vol. 16, no. 4, pp. 743-749, Nov. 2001. https://doi.org/10.1109/59.962421
J. B. Park, K. S. Lee, J. R. Shin, and K. Y. Lee, "A particle swarm optimization for economic dispatch with nonsmooth cost functions," IEEE Transactions on Power Systems, vol. 20, no. 1, pp. 34-42, Feb. 2005. https://doi.org/10.1109/TPWRS.2004.831275
G. L. Torres and V. H. Quintana, "On a nonlinear multiple-centrality-corrections interior-point method for optimal power flow," IEEE Transactions on Power Systems, vol. 16, no. 2, pp. 222-228, May 2001. https://doi.org/10.1109/59.918290
P. Jain, K. K. Swarnkar, and S. Wadhwani, "Prohibited Operating Zones Constraint with Economic Load Dispatch Using Genetic Algorithm," International Journal of Engineering and Innovative Technology, vol. 1, no. 3, pp. 179-183, Jan. 2012.
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. https://doi.org/10.48084/etasr.2098
N. Pandit, A. Tripathi, S. Tapaswi, and M. Pandit, "An improved bacterial foraging algorithm for combined static/dynamic environmental economic dispatch," Applied Soft Computing, vol. 12, no. 11, pp. 3500-3513, Nov. 2012. https://doi.org/10.1016/j.asoc.2012.06.011
K. Tlijani, T. Guesmi, and H. H. Abdallah, "Extended Dynamic Economic Environmental Dispatch using Multi-Objective Particle Swarm Optimization," International Journal on Electrical Engineering and Informatics, vol. 8, no. 1, pp. 117-131, Mar. 2016. https://doi.org/10.15676/ijeei.2016.8.1.9
A. Srinivasa Reddy and K. Vaisakh, "Shuffled differential evolution for economic dispatch with valve point loading effects," International Journal of Electrical Power & Energy Systems, vol. 46, pp. 342-352, Mar. 2013. https://doi.org/10.1016/j.ijepes.2012.10.012
I. Ziane, F. Benhamida, A. Graa, "Simulated annealing algorithm for combined economic and emission power dispatch using max/max price penalty factor," Neural Comput Appl, vol. 28, no. 1, pp. 197-205, 2017. https://doi.org/10.1007/s00521-016-2335-3
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. https://doi.org/10.1109/TSTE.2010.2044817
M. H. Alham, M. Elshahed, D. K. Ibrahim, and E. E. D. Abo El Zahab, "A dynamic economic emission dispatch considering wind power uncertainty incorporating energy storage system and demand side management," Renewable Energy, vol. 96, pp. 800-811, Oct. 2016. https://doi.org/10.1016/j.renene.2016.05.012
L. Wang and C. Singh, "Balancing risk and cost in fuzzy economic dispatch including wind power penetration based on particle swarm optimization," Electric Power Systems Research, vol. 78, no. 8, pp. 1361-1368, Aug. 2008. https://doi.org/10.1016/j.epsr.2007.12.005
F. Yao, Z. Y. Dong, K. Meng, Z. Xu, H. H.-C. Iu, and K. P. Wong, "Quantum-Inspired Particle Swarm Optimization for Power System Operations Considering Wind Power Uncertainty and Carbon Tax in Australia," IEEE Transactions on Industrial Informatics, vol. 8, no. 4, pp. 880-888, Nov. 2012. https://doi.org/10.1109/TII.2012.2210431
M. H. Aliabadi, S. Javadi, E. Tafehi, and A. Bakhtiari, "Risk Analysis and Economic Load Dispatch Evaluation of Network with High Wind Power Penetration," International Journal of Smart Electrical Engineering, vol. 3, no. 02, pp. 79-85, Apr. 2014.
S. Jiang, C. Zhang, W. Wu, and S. Chen, "Combined Economic and Emission Dispatch Problem of Wind-Thermal Power System Using Gravitational Particle Swarm Optimization Algorithm," Mathematical Problems in Engineering, vol. 2019, Nov. 2019, Art. no. e5679361. https://doi.org/10.1155/2019/5679361
V. K. Jadoun, V. C. Pandey, N. Gupta, K. R. Niazi, and A. Swarnkar, "Integration of renewable energy sources in dynamic economic load dispatch problem using an improved fireworks algorithm," IET Renewable Power Generation, vol. 12, no. 9, pp. 1004-1011, 2018. https://doi.org/10.1049/iet-rpg.2017.0744
T. Guesmi, A. Farah, I. Marouani, B. Alshammari, and H. H. Abdallah, "Chaotic sine-cosine algorithm for chance-constrained economic emission dispatch problem including wind energy," IET Renewable Power Generation, vol. 14, no. 10, pp. 1808-1821, 2020. https://doi.org/10.1049/iet-rpg.2019.1081
J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proceedings of ICNN'95 - International Conference on Neural Networks, Nov. 1995, vol. 4, pp. 1942-1948 vol.4.
Z. Bo and C. Yi-jia, "Multiple objective particle swarm optimization technique for economic load dispatch," Journal of Zhejiang University-SCIENCE A, vol. 6, no. 5, pp. 420-427, May 2005. https://doi.org/10.1631/jzus.2005.A0420
A. Mahor, V. Prasad, and S. Rangnekar, "Economic dispatch using particle swarm optimization: A review," Renewable and Sustainable Energy Reviews, vol. 13, no. 8, pp. 2134-2141, Oct. 2009. https://doi.org/10.1016/j.rser.2009.03.007
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Copyright (c) 2021 G. A. Alshammari, F. A. Alshammari, T. Guesmi, B. M. Alshammari, A. S. Alshammari, N. A. Alshammari
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