Strength Pareto Evolutionary Algorithm for the Dynamic Economic Emission Dispatch Problem incorporating Wind Farms and Energy Storage Systems
Intermittent and stochastic characteristics of wind energy sources cause many challenges for the existing power networks. One of these challenges is the violation of the energy balance constraint due to the high penetration of wind power. The use of Energy Storage Systems (ESS) can facilitate the high penetration of wind power and mitigate the effect of its intermittency. Within this context, ESS incorporate the Dynamic Economic Emission Dispatch (DEED) problem. The problem is formulated as a multi-objective problem and the Strength Pareto Evolutionary Algorithm (SPEA) is used for its resolution. Simulations were carried out on a well-known ten-unit system and the results show the importance of using ESS in reducing the total production cost of electricity and total emissions.
L. Han, R. Zhang, K. Chen, “A coordinated dispatch method for energy storage power system considering wind power ramp event”, Applied Soft Computing, Vol. 84, Article ID 105732, 2019
W. Wang, R. Ma, H. Xu, H. Wang, K. Cao, L. Chen, Z. Ren, “Method of energy storage system sizing for wind power generation integration”, IEEE PES Asia-Pacific Power and Energy Engineering Conference, Xi'an, China, October 25-28, 2016
J. Wu, Y. Lin, “Economic dispatch including wind power injection”, in: Proceedings of ISES World Congress 2007, Vol. I-V, Springer, 2007
F. Benhamida, Y. Salhi, I. Ziane, S. Souag, R. Belhachem, A. Bendaoud, “A PSO algorithm for the economic load dispatch including a renewable wind energy”, 3rd International Conference on Systems and Control, Algiers, Algeria, October 29-31, 2013
K. K. Vishwakarma, H. M. Dubey, “Simulated annealing based optimization for solving large scale economic load dispatch problems”, International Journal of Engineering Research & Technology, Vol. 1, No. 3, pp. 1-8, 2012
R. V. Pandi, B. K. Panigrahi, “Dynamic economic load dispatch using hybrid swarm intelligence base harmony search algorithm”, Expert Systems with Applications, Vol. 38, No. 7, pp. 8509-8514, 2011
M. Younes, R. L. Kherfene, F. Khodja, “Environmental/economic power dispatch problem/renewable energy using firefly algorithm”, International Conference on Environment, Energy, Ecosystems and Development, Venice, Italy, September 28-30, 2013
P. K. Roy, S. Hazra, “Economic emission dispatch for wind-fossil-fuel-based power system using chemical reaction optimisation”, International Transactions on Electrical Energy Systems, Vol. 25, No. 12, pp. 3248-3274, 2014
Z. Wang, C. Shen, F. Liu, “A conditional model of wind power forecast errors and its application in scenario generation”, Applied Energy, Vol. 212, pp. 771-785, 2018
L. Han, R. Zhang, X. Wang, Y. Dong, “Multi-time scale rolling economic dispatch for wind/storage power system based on forecast error feature extraction”, Energies, Vol. 11, No. 8, Article ID 2124, 2018
Q. Wang, Y. Guan, J. Wang, “A chance-constrained two-stage stochastic program for unit commitment with uncertain wind power output”, IEEE Transactions on Power Systems, Vol. 27, No. 1, pp. 206-215, 2012
M. A. O. Vazquez, D. S. Kirschen, “Estimating the spinning reserve requirements in systems with significant wind power generation penetration”, IEEE Transactions on Power Systems, Vol. 24, No. 1, pp. 114-124, 2009
P. Xiong, P. Jirutitijaroen, C. Singh, “A distributionally robust optimization model for unit commitment considering uncertain wind power generation”, IEEE Transactions on Power Systems, Vol. 32, No. 1, pp. 39-49, 2017
Y. Hu, Y. Li, M. Xu, L. Zhou, M. Cui, “A chance-constrained economic dispatch model in wind-thermal-energy storage system”, Energies, Vol. 10, No. 3, Article ID 326, 2017
X. Liu, 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, 2010
S. Hazra, P. K. Roy, “Quasi-oppositional chemical reaction optimization for combined economic emission dispatch in power system considering wind power uncertainties”, Renewable Energy Focus, Vol. 31, pp. 45-62, 2019
H. Lan, H. Yin, S. Wen, Y. Y. Hong, D. C. Yu, L. Zhang, “Electrical energy forecasting and optimal allocation of ESS in a hybrid wind-diesel power system”, Applied Sciences, Vol. 7, No. 2, Article ID 155, 2017
M. H. Alham, M. Elshahed, D. K. Ibrahim, E. E. D. A. E. 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, 2016
K. Alqunun, P. A. Crossley, “Rated energy impact of BESS on total operation cost in a microgrid”, International Conference on Smart Energy Grid Engineering, Oshawa, Canada, August 21-24, 2016
B. Xiao, Y. Zhang, J. Han, D. Liu, M. Wang, G. Yan, “A multi-energy complementary coordinated dispatch method for integrated system of wind-photovoltaic-hydro-thermal-energy storage”, International Transactions on Electrical Energy Systems, Vol. 29, No. 7, Article ID e12005, 2019
A. Torchani, A. Boudjemline, H. Gasmi, Y. Bouazzi, T. Guesmi, “Dynamic economic/environmental dispatch problem considering prohibited operating zones”, Engineering, Technology & Applied Science Research, Vol. 9, No. 5, pp. 4586-4590, 2019
E. Zitzler, L. Thiele, “Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach”, IEEE Transactions on Evolutionary Computation, Vol. 3, No. 4, pp. 257-271, 1999
I. Marouani, A. Boudjemline, T. Guesmi, H. H. Abdallah, “A modified artificial bee colony for the nonsmooth dynamic economic/environmental dispatch”, Engineering, Technology & Applied Science Research, Vol. 8, No. 5, pp. 3321-3328, 2018
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, 2008
N. Pandit, A. Tripathi, S. Tapaswi, M. Pandit, “An improved bacterial foraging algorithm for combined static/dynamic environmental economic dispatch”, Applied Soft Computing, Vol. 12, No. 11, pp. 3500–3513, 2012
MetricsAbstract Views: 91
PDF Downloads: 68
Copyright (c) 2020 Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.