Dynamic Economic/Environmental Dispatch Problem Considering Prohibited Operating Zones
Along with economic dispatch, emission dispatch has become a key problem under market conditions. Thus, the combination of the above problems in one problem called economic emission dispatch (EED) problem became inevitable. However, due to the dynamic nature of today’s network loads, it is required to schedule the thermal unit outputs in real-time according to the variation of power demands during a certain time period. Within this context, this paper presents an elitist technique, the second version of the non-dominated sorting genetic algorithm (NSAGII) for solving the dynamic economic emission dispatch (DEED) problem. Several equality and inequality constraints, such as valve point loading effects, ramp rate limits and prohibited operating zones (POZ), are taken into account. Therefore, the DEED problem is considered as a non-convex optimization problem with multiple local minima with higher-order non-linearities and discontinuities. A fuzzy-based membership function value assignment method is suggested to provide the best compromise solution from the Pareto front. The effectiveness of the proposed approach is verified on the standard power system with ten thermal units.
Keywords:dynamic environmental/economic dispatch, prohibited operating zones, multi-objective optimization, non-dominated sorting genetic algorithm
F. Milano, “An open source power system analysis toolbox”, IEEE Transactions on Power Systems, Vol. 20, No. 3, pp. 1199-1206, 2005 DOI: https://doi.org/10.1109/TPWRS.2005.851911
R. D. Zimmerman, C. E. M. Sanchez, R. J. Thomas, “Matpower steady-state operations, planning and analysis tools for power systems research and education”, IEEE Transactions on Power Systems, Vol. 26, No. 1, pp. 12-19, 2011 DOI: https://doi.org/10.1109/TPWRS.2010.2051168
S. Boudab, N. Golea, “Combined economic-emission dispatch problem: Dynamic neural networks solution approach”, Journal of Renewable and Sustainable Energy, Vol. 9, No. 3, Article ID 035503, 2017 DOI: https://doi.org/10.1063/1.4985089
M. Basu, “Economic environmental dispatch using multi-objective differential evolution”, Applied Soft Computing, Vol. 11, No. 2, pp. 2845-2853, 2011 DOI: https://doi.org/10.1016/j.asoc.2010.11.014
M. A. Abido, “Multiobjective evolutionary algorithms for electric power dispatch problem”, IEEE Transactions on Evolutionary Computation, Vol. 10, No. 3, pp. 315-329, 2006 DOI: https://doi.org/10.1109/TEVC.2005.857073
S. Sivasubramani, K. S. Swarup, “Environmental/economic dispatch using multi-objective harmony search algorithm”, Electric Power Systems Research, Vol. 81, No. 9, pp. 1778-1785, 2011 DOI: https://doi.org/10.1016/j.epsr.2011.04.007
G. C. Liao, “Solve environmental economic dispatch of smart microgrid containing distributed generation system-using chaotic quantum genetic algorithm”, International Journal of Electrical Power & Energy Systems, Vol. 43, No. 1, pp. 779-787, 2012 DOI: https://doi.org/10.1016/j.ijepes.2012.06.040
B. Hadji, B. Mahdad, K. Srairi, N. Mancer, “Multi-objective economic emission dispatch solution using dance bee colony with dynamic step size”, Energy Procedia, Vol. 74, pp. 65-76, 2015 DOI: https://doi.org/10.1016/j.egypro.2015.07.524
K. Tlijani, T. Guesmi, 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, 2016 DOI: https://doi.org/10.15676/ijeei.2016.8.1.9
H. Ma, Z. Yang, P. You, M. Fei, “Multi-objective biogeography-based optimization for dynamic economic emission load dispatch considering plug-in electric vehicles charging”, Energy, Vol. 135, pp. 101–111, 2017 DOI: https://doi.org/10.1016/j.energy.2017.06.102
S. Hemamalini, S. P. Simon, “Dynamic economic dispatch using artificial bee colony algorithm for units with valve-point effect”, European Transactions on Electrical Power, Vol. 21, No. 1, pp. 70-81, 2011 DOI: https://doi.org/10.1002/etep.413
C. K. Panigrahi, P. K. Chattopadhyay, R. N. Chakrabarti, M. Basu, “Simulated annealing technique for dynamic economic dispatch”, Electric Power Components and Systems, Vol. 34, No. 5, pp. 577-586, 2006 DOI: https://doi.org/10.1080/15325000500360843
R. Balamurugan, S. Subramanian, “An improved differential evolution based dynamic economic dispatch with nonsmooth fuel cost function”, Journal of Electrical Systems, Vol. 3, No. 3, pp. 151-161, 2007
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 DOI: https://doi.org/10.1016/j.asoc.2012.06.011
H. Rezaie, M. H. K. Rahbar, B. Vahidi, H. Rastegar, “Solution of combined economic and emission dispatch problem using anovel chaotic improved harmony search algorithm”, Journal of Computational Design and Engineering, Vol. 6, No. 3, pp. 447-467, 2019 DOI: https://doi.org/10.1016/j.jcde.2018.08.001
G. Irisarri, L. M. Kimball, K. A. Clements, A. Bagchi, P. W. Davis, “Economic dispatch with network and ramping constraints via interior point methods”, IEEE Transactions on Power Systems, Vol. 13, No. 1, pp. 236-242, 1998 DOI: https://doi.org/10.1109/59.651641
S. Ganjefar, M. Tofighi, “Dynamic economic dispatch solution using an improved genetic algorithm with non-stationary penalty functions”, European Transactions on Electrical Power, Vol. 21, No. 3, pp. 1480–1492, 2011 DOI: https://doi.org/10.1002/etep.520
W. M. Lin, F. S. Cheng, M. T. Tsay, “An improved tabu search for economic dispatch with multiple minima”, IEEE Transactions on Power Systems, Vol. 17, No. 1, pp. 108-112, 2002 DOI: https://doi.org/10.1109/59.982200
K. Mason, J. Duggan, E. Howley, “Multi-objective dynamic economic emission dispatch using particle swarm optimisation variants”, Neurocomputing, Vol. 270, pp. 188–197, 2017 DOI: https://doi.org/10.1016/j.neucom.2017.03.086
M. Basu, “Particle swarm optimization based goal-attainment method for dynamic economic emission dispatch”, Electric Power Components and Systems, Vol. 34, No. 9, pp. 1015-1025, 2006 DOI: https://doi.org/10.1080/15325000600596759
K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II”, IEEE Transactions on Evolutionary Computation, Vol. 6, No. 2, pp. 182-197, 2002 DOI: https://doi.org/10.1109/4235.996017
M. Z. Jahromi, M. M. H. Bioki, M. Rashidinejad, R. Fadaeinedjad, “Solution to the unit commitment problem using an artificial neural network”, Turkish Journal of Electrical Engineering and Computer Sciences, Vol. 21, pp. 198-212, 2013
How to Cite
MetricsAbstract Views: 527
PDF Downloads: 307
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.