Abstract
Since decades, the main aim for the researchers is to get reliable and inexpensive electricity out of available resources. Till now, different economic dispatch methods have been developed to meet the challenges of continuous and sustainable electricity production at optimized cost. Due to the increase of public awareness on environmental protection, the utilities have forced to use renewable energy sources to modify the operation strategies to reduce the pollution and atmospheric emissions produced due to power plants. This paper represents the allocation of dynamic economic emission dispatch (DEED) problem involving thermal and photovoltaic (PV) generation system as hybrid dynamic economic emission dispatch (HDEED) to meet the minimization of both the cost of fuel and emission simultaneously. Whale optimization algorithm (WOA) is used to minimize the total cost of operation and emission levels while dispatching power to committed generation units considering all operational constraints. To show the efficiency, the proposed technique is applied to solve the dynamic economic emission dispatch problem on 5- and 10-unit test systems. The technique is also used to solve the HDEED problem on IEEE 30-bus 6-unit and IEEE 57-bus 7-unit test system with and without renewable generation. The results obtained after injection of photovoltaic power into existing power systems show the effectiveness of the proposed techniques to reduce the cost of operation and emission levels.
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References
Aghaei J, Niknam T, Abarghooee RA, Arroyo JM (2013) Scenario based dynamic economic emission dispatch considering load & wind power uncertainties. Int J Electr Power Energy Syst 47:351–367. https://doi.org/10.1016/j.ijepes.2012.10.069
Alsumait JS, Qasem M, Sykalski JK, Al-Othman AK (2010) An improved pattern search based algorithm to solve the dynamic economic dispatch problem with valve point effect. Energy Convers Manage 51(10):2062–2067. https://doi.org/10.1016/j.enconman.2010.02.039
Balamurugan R, Subramanian S (2007) An improved differential evolution based dynamic economic dispatch with nonsmooth fuel cost function. J Electr Syst 3–3(2007): 151–161. https://journal.esrgroups.org/jes/papers/3_3_4.pdf
Basu M (2006) Particle swarm optimization based goal attainment method for dynamic economic emission dispatch. Electr Power Component Syst 34(9):1015–1025. https://doi.org/10.1080/15325000600596759
Basu M (2008) Dynamic economic emission dispatch using non-dominated sorting genetic algorithm-II. Int J Electr Power Energy Syst 30(2):140–149. https://doi.org/10.1016/j.ijepes.2007.06.009
Basu M (2011) Artificial immune system for dynamic economic dispatch. Int J Electr Power Energy Syst 33(1):131–136. https://doi.org/10.1016/j.ijepes.2010.06.019
Bracale A, Caramia P, Carpinelli G, Rita A, Fazio D, Ferruzzi G (2013) A Bayesian method for short term probabilistic forecasting of photovoltaic generation in smart grid operation and control. Energies 6(2):733–747. https://doi.org/10.3390/en6020733
Civicioglu P (2013) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219(15):8121–8144. https://doi.org/10.1016/j.amc.2013.02.017
Conti S, Crimi T, Raiti S, Tina G, Vagliasindi U (2002) Probabilistic approach to assess the long-term performance of grid connected PV systems. In: Proceedings of the 7th international conference on probabilistic methods applied to power systems (PMAPS, September-2002), Naples, Italy, pp 22–26
Elaiw AM, Xia X, Shehata AM (2013) Hybrid DE-SQP & hybrid PSO-SQP methods for solving dynamic economic emission dispatch problem with valve point effects. Electr Power Syst Res 103:192–200. https://doi.org/10.1016/j.epsr.2013.05.015
Hemamalini S, Simon SP (2010) Dynamic economic dispatch using Maclaurin series based Lagrangian method. Energy Convers Manage 51(11):2212–2219. https://doi.org/10.1016/j.enconman.2010.03.015
Hemamalini S, Simon SP (2011a) Dynamic economic dispatch using artificial bee colony algorithm for units with valve point effect. Eur Trans Electr Power 21(1):70–81. https://doi.org/10.1002/etep.413
Hemamalini S, Simon SP (2011b) Dynamic economic dispatch using artificial immune system for units with valve point effect. Int J Electr Power Energy Syst 33(4):868–874. https://doi.org/10.1016/j.ijepes.2010.12.017
Hetzer J, Yu DC, Bhattarai K (2008) An economic dispatch model incorporating wind power. IEEE Trans Energy Convers 23(2):603–611. https://doi.org/10.1109/TEC.2007.914171
Jiang X, Zhou J, Wang H, Zhang Y (2013) Dynamic environmental economic dispatch using multiobjective differential evolution algorithm with expanded double selection and adaptive random restart. Int J Electr Power Energy Syst 49:399–407. https://doi.org/10.1016/j.ijepes.2013.01.009
Karthikeyan SP, Palanisamy K, Rani C, Raglend IJ, Kothari DP (2009) Security constrained unit commitment problem with operational power flow & environmental constraints. WSEAS Trans Power Syst 4(2):53–66
Khan et al (2015) Combined emission economic dispatch of power system including solar photovoltaic generation. Energy Convers Manage 92:82–91. https://doi.org/10.1016/j.enconman.2014.12.029
Mason K, Duggan J, Howley E (2018) Amulti-objective neural network trained with differential evolution for dynamic economic emission dispatch. Electr Power Energy Syst 100(6):201–221. https://doi.org/10.1016/j.ijepes.2018.02.021
Mirjalili S, Lewis A (2016) The Whale optimization algorithm. Adv Eng Softw 95:51–67
Niknam T, Golestaneh F (2012) Enhanced adaptive particle swarm optimization algorithm for dynamic economic dispatch of units considering valve point effects and ramp rates. IET Gener Trans Distrib 6(5):424–435. https://doi.org/10.1049/iet-gtd.2011.0219
Niu Q, Zhang H, Li K, Irwin GW (2014) An efficient harmony search with new pitch adjustment for dynamic economic dispatch. Energy 65:25–43. https://doi.org/10.1016/j.energy.2013.10.085
Orgill JF, Hollands KGT (1977) Correlation equation for hourly diffuse radiation on a horizontal surface. Sol Energy 19(4):357–359. https://doi.org/10.1016/0038-092X(77)90006-8
Pandi VR, Panigrahi BK (2011) Dynamic economic load dispatch using hybrid swarm intelligence based harmony search algorithm. Expert Syst Appl 38(7):8509–8514. https://doi.org/10.1016/j.eswa.2011.01.050
Pandit N, Tripathi A, Tapaswi S, Pandit M (2012) An improved bacterial foraging algorithm for combined static/dynamic environmental economic dispatch. Appl Soft Comput 12(11):3500–3513. https://doi.org/10.1016/j.asoc.2012.06.011
Panigrahi CK, Chattopadhyay PK, Chakrabarti RN, Basu M (2006) Simulated annealing technique for dynamic economic dispatch. Electr Power Components Syst 34(5):577–586. https://doi.org/10.1080/15325000500360843
Panigrahi BK, Pandi VR, Das S (2008) Adaptive particle swarm optimization approach for static and dynamic economic load dispatch. Energy Convers Manage 49(6):1407–1415. https://doi.org/10.1016/j.enconman.2007.12.023
Papoulis A, Pillai SU (2002) Probability, random variables and stochastic processes, 4th edn. Tata McGraw-Hill Education. ISBN: 978-0-07-048658-4
Roy PK, Bhui S (2016) A multi objective hybrid evolutionary algorithm for dynamic economic emission load dispatch. Int Trans Electr Energy Syst 26(1):49–78. https://doi.org/10.1002/etep.2066
Tina G, Gagliano S (2011) Probabilistic analysis of weather data for a hybrid solar/wind energy system. Int J Energy Res 35(3):221–232. https://doi.org/10.1002/er.1686
Wan C, Xu Z, Pinson P, Dong ZY, Wong KP (2014) Probabilistic forecasting of wind power generation using extreme learning machine. IEEE Trans Power Syst 29(3):1033–1044. https://doi.org/10.1109/TPWRS.2013.2287871
Yang Z, Niu Q, Xue Y, Foley A (2014) A self learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads. Modern Optim Tech Power Syst Operat Plann 2:298–307. https://doi.org/10.1007/s40565-014-0087-6
Zhang H, Yue D, Xie X, Hu S, Weng S (2015) Multi elite guide hybrid differential evolution with simulated annealing technique for dynamic economic emission dispatch. Appl Soft Comput 34:312–323. https://doi.org/10.1016/j.asoc.2015.05.012
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Padhi, S., Panigrahi, B.P. & Dash, D. Assessment of Dynamic Economic and Emission Dispatch Problem using WOA in Networked Grids with Photovoltaic Power Injection. Trans Indian Natl. Acad. Eng. 5, 675–696 (2020). https://doi.org/10.1007/s41403-020-00162-2
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DOI: https://doi.org/10.1007/s41403-020-00162-2