Please wait a minute...
Frontiers in Energy

ISSN 2095-1701

ISSN 2095-1698(Online)

CN 11-6017/TK

邮发代号 80-972

2019 Impact Factor: 2.657

Frontiers in Energy  2015, Vol. 9 Issue (4): 426-432   https://doi.org/10.1007/s11708-015-0376-4
  本期目录
Optimal operation of energy at hydrothermal power plants by simultaneous minimization of pollution and costs using improved ABC algorithm
Homayoun EBRAHIMIAN1,Bahman TAHERI1,Nasser YOUSEFI2,*()
1. Department of Engineering, Ardabil Branch, Islamic Azad University, Ardabil 0451, Iran
2. Young Researchers and Elite Club, Ardabil Branch, Islamic Azad University, Ardabil 0451, Iran
 全文: PDF(524 KB)   HTML
Abstract

The aim of this paper is simultaneous minimization of hydrothermal units to reach the best solution by employing an improved artificial bee colony (ABC) algorithm in a multi-objective function consisting of economic dispatch (ED) considering the valve-point effect and pollution function in power systems in view of the hot water of the hydro system. In this type of optimization problem, all practical constraints of units were taken into account as much as possible in order to comply with the reality. These constraints include the maximum and minimum output power of units, the constraints caused by the balance between supply and demand, the impact of pollution, water balance, uneven production curve considering the valve-point effect and system losses. The proposed algorithm is applied on the studied system, and the obtained results indifferent operating conditions are analyzed. To investigate in various operating conditions, different load profiles in 12 h are taken into account. The obtained results are compared with those of the other methods including the genetic algorithm (GA), the Basu technique, and the improved genetic algorithm. Fast convergence is one of this improved algorithm features.

Key wordspractical constraints of units    pollution function    inlet steam valve    up-ramp rate of units    improved ABC algorithm
收稿日期: 2014-12-23      出版日期: 2015-11-04
Corresponding Author(s): Nasser YOUSEFI   
 引用本文:   
. [J]. Frontiers in Energy, 2015, 9(4): 426-432.
Homayoun EBRAHIMIAN,Bahman TAHERI,Nasser YOUSEFI. Optimal operation of energy at hydrothermal power plants by simultaneous minimization of pollution and costs using improved ABC algorithm. Front. Energy, 2015, 9(4): 426-432.
 链接本文:  
https://academic.hep.com.cn/fie/CN/10.1007/s11708-015-0376-4
https://academic.hep.com.cn/fie/CN/Y2015/V9/I4/426
Fig.1  
Fig.2  
Fig.3  
Methods No. Hydro plant Thermal unit i=2Nk+NiPmii Plm/MW Cost/(?$·MW−1) Emission
Pm1/MW Pm2/MW Pm3/MW Pm4/MW Pm5/MW Pm6/MW
Basu m=1 ?201.3129 326.508 95.983 ?112.0962 ?42.175 140.175 ?918.548 ?18.3678 68783.2 25245.9
m=2 232.171 326.782 21.3471 ?110.7822 ?206.4700 228.76? 1126.31 26.788 65357.1 25898.8
m=3 196.144 440.219 97.480 110.699 ?40.037 139.745 1024.00 24.110 67567.1 28890.8
GA-MU m=1 ?235.2796 ?215.7313 94.6682 ?112.6695 ?209.5843 ??50.0003 ?917.933 17.933 ?6420.9 25201.1
m=2 154.400 499.778 98.4884 ?112.0195 124.228 ?284.0341 1129.36 29.267 65123.4 25239.1
m=3 236.687 409.076 20.000 111.930 106.874 139.628 1024.19 24.196 67000.1 28567.1
IGAMU m=1 193.778 299.739 47.038 112.665 124.905 139.755 ?918.082 ?18.0825 64031.8 25021.8
m=2 ?220.8464 430.697 99.009 112.875 124.908 139.755 ?1128.097 28.097 65001.1 25010.6
m=3 229.709 399.533 99.726 ?30.002 124.907 139.755 ?1023.063 23.632 66789.4 28019.3
MOIABC m=1 237.879 216.811 93.072 112.001 200.782 ?54.874 ?916.073 ?16.0733 63561.5 25145.7
m=2 ?151.9812 500.000 99.234 111.781 125.000 139.561 1127.73 27.732 63672.2 25671.9
m=3 237.782 401.652 99.000 113.123 210.927 228.176 1019.21 19.212 65123.8 27781.4
Tab.1  
Fig.4  
Methods No. Hydro plant Thermal unit i=1Nh+NiPmii Plm/MW Cost/(?$·MW−1) Emission
Pm1/MW Pm2/MW Pm3/MW Pm4/MW Pm5/MW Pm6/MW
Basu m=1 ?173.2055 312.828 72.309 133.738? 134.910 90.980 ??917.9703 ?18.1606 65783.9 24123.3
m=2 249.999 398.003 80.724 145.66?? 150.681 102.703? 1127.754 ?28.0453 65893.5 25891.2
m=3 202.577 370.520 ?75.1011 138.0860 140.728 95.761 1022.775 23.009 67980.5 27891.6
GA-MU m=1 ?172.2377 ?310.9098 ?73.1046 135.0053 ?135.6157 ?91.2570 ??918.1301 ?18.1301 65900.2 24672.1
m=2 244.845 409.64? 80.452 143.5200 148.302 101.4500 1128.228 ?28.2286 66112.5 24230.1
m=3 208.959 360.084 76.505 138.692? ?142.0318 ?96.6060 1022.87? 22.879 67897.3 27892.2
IGAMU m=1 173.205 312.826 72.305 133.683? 134.910 ?90.9080 ?917.970 ?18.1608 65238.2 24783.2
m=2 ?249.9991 398.002 ?80.7249 145.451? ?150.6817 102.7040 ?1127.758 ?28.0435 66389.6 24389.3
m=3 202.000 370.520 75.099 1380850 140.728 ?95.7617 1022.775 ?23.0095 67378.5 27190.5
MOIABC m=1 171.672 311.267 ?75.7813 133.405? 133.778 90.000 ?916.129 16.129 64290.4 23989.4
m=2 248.672 398.371 79.689 144.78?? ?151.2001 100.2340 1122.78? 22.930 64223.6 24239.8
m=3 207.671 366.367 74.235 187.895? 187.892 ?94.9032 1020.798 ?20.7812 65989.9 26785.6
Tab.2  
Fig.5  
Fig.6  
Fig.7  
Fig.8  
Fig.9  
1 Wu  L H, Wang  Y N, Yuan  X F, Zhou  S W. Environmental/economic power dispatch problem using multi-objective differential evolution algorithm. Electric Power Systems Research, 2010, 80(9): 1171–1181
https://doi.org/10.1016/j.epsr.2010.03.010
2 Lamont  J W, Obessis  E V. Emission dispatch models and algorithms for the 1990’s. IEEE Transactions on Power Systems, 1995, 10(2): 941–947
https://doi.org/10.1109/59.387937
3 Basu  M. A simulated annealing-based goal attainment method for economic emission load is patch of fixed head hydrothermal power systems. International Journal of Electrical Power & Energy Systems, 2005, 27(2): 147–153
https://doi.org/10.1016/j.ijepes.2004.09.004
4 Venkatesh  P, Gnanadass  R, Padhy  N P. Comparison and application of evolutionary programming techniques to combined economic emission dispatch with line flow constraints. IEEE Transactions on Power Systems, 2003, 18(2): 688–697
https://doi.org/10.1109/TPWRS.2003.811008
5 Huang  C M, Yang  H T, Huang  C L. Bi-Objective power dispatch using fuzzy satisfaction-maximizing decision approach. IEEE Transactions on Power Systems, 1997, 12(4): 1715–1721
https://doi.org/10.1109/59.627881
6 Coello Coello  C A. A comprehensive survey of evolutionary-based multi objective optimization techniques. Knowledge and Information Systems, 1999, 1(3): 269–308
https://doi.org/10.1007/BF03325101
7 Muslu  M. Economic dispatch with environmental considerations: tradeoff curves and emission reduction rates. International Journal of Electrical Power & Energy Systems, 2004, 71: 153–158
8 El-hawary  M E, Landrigan  J K. Optimum operation of fixed-head hydro-thermal electric power systems: Powell’s hybrid method versus Newton-Raphson method. IEEE Transactions on Power Apparatus and Systems, 1982, PAS-101(3): 547–554
https://doi.org/10.1109/TPAS.1982.317267
9 Zaghlool  M F, Trutt  F C. Efficient methods for optimal scheduling of fixed head hydrothermal power systems. IEEE Transactions on Power Systems, 1988, 3(1): 24–30
https://doi.org/10.1109/59.43176
10 Abido  M A. Environmental/economic power dispatch using multiobjective evolutionary algorithms. IEEE Transactions on Power Systems, 2003, 18(4): 1529–1537
https://doi.org/10.1109/TPWRS.2003.818693
11 Chiang  C L. Optimal economic emission dispatch of hydrothermal power systems. International Journal of Electrical Power & Energy Systems, 2007, 29(6): 462–469
https://doi.org/10.1016/j.ijepes.2006.11.004
12 Karaboga  D, Basturk  B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 2007, 39(3): 459–471
https://doi.org/10.1007/s10898-007-9149-x
13 Tarafdar hagh  M, Ebrahimian  H, Ghadimi  N. Hybrid intelligent water drop bundled wavelet neural network to solve the islanding detection by inverter based DG. Frontiers in Energy, 2015, 9(1): 75–90
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed