Optimal power generation for wind-hydro-thermal system using meta-heuristic algorithms

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 10, No. 5, October 2020, pp. 5123~5130
ISSN: 2088-8708, DOI: 10.11591/ijece.v10i5.pp5123-5130  5123
Journal homepage: http://ijece.iaescore.com/index.php/IJECE
Optimal power generation for wind-hydro-thermal system
using meta-heuristic algorithms
Thuan Thanh Nguyen1
, Van-Duc Phan2
, Bach Hoang Dinh3
, Tan Minh Phan4
, Thang Trung Nguyen5
1
Faculty of Electrical Engineering Technology, Industrial University of Ho Chi Minh City, Vietnam
2
Faculty of Automobile Technology, Van Lang University, Vietnam
3,5
Power System Optimization Research Group, Faculty of Electrical and Electronics Engineering,
Ton Duc Thang University, Vietnam
4
Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Vietnam
Article Info ABSTRACT
Article history:
Received Mar 25, 2019
Revised Apr 27, 2020
Accepted May 8, 2020
In this paper, cuckoo search algorithm (CSA) is suggested for determining
optimal operation parameters of the combined wind turbine and
hydrothermal system (CWHTS) in order to minimize total fuel cost of all
operating thermal power plants while all constraints of plants and system are
exactly satisfied. In addition to CSA, Particle swarm optimization (PSO),
PSO with constriction factor and inertia weight factor (FCIW-PSO)
and social ski-driver (SSD) are also implemented for comparisons.
The CWHTS is optimally scheduled over twenty-four one-hour interval and
total cost of producing power energy is employed for comparison.
Via numerical results and graphical results, it indicates CSA can reach much
better results than other ones in terms of lower total cost, higher success rate
and faster search process. Consequently, the conclusion is confirmed that
CSA is a very efficient method for the problem of determining optimal
operation parameters of CWHTS.
Keywords:
Cuckoo search algorithm
Fitness function
Hydrothermal system
Total fuel cost
Wind turbine
Copyright © 2020 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Thang Trung Nguyen,
Power System Optimization Research Group,
Faculty of Electrical and Electronics Engineering,
Ton Duc Thang University,
19 Nguyen Huu Tho street, Tan Phong ward, District 7, Ho Chi Minh City, Viet Nam.
Email: nguyentrungthang@tdtu.edu.vn
NOMENCLATURE
Ntp Number of thermal units
Nin Number of scheduled intervals
ki, mi, ni Coefficient of fuel cost function
𝑃𝑇𝑖,𝑗, PHk,j, PWw,j Generation of the ith thermal unit, the kth hydro unit and the wth wind turbine at
the jth interval
Ntp, Nhp, Nwt, Nin Number of thermal units, hydro units, wind turbines and intervals.
Pload,j, Ploss,j Power of load and loss at the jth interval
PWw, PWw,rate Generation and rated generation of the wth wind turbine
WV, WVrate, WVcut-in,
WVcut-out
Wind speed, rated wind speed, cut-in speed and cut-out speed
PWw,min, PWw,max Minimum and maximum generation of the wth wind turbine
Xk ,Yk, Zk coefficients of the kth hydro unit’s generation
Qk,min, Qk,max Minimum and maximum discharge of the kth hydro unit
Qk,j Discharge of the kth hydro unit at the jth interval
Wavai,k available water for power generation over the scheduled intervals

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 5, October 2020 : 5123 - 5130
5124
PHk,min, PHk,max Minimum and maximum power generation of the kth hydro unit
xSol ,
new
xSol The xth current solution and new solution
xFit ,
new
xFit Fitness function the the xth current and new solution
1. INTRODUCTION
Hydrothermal system scheduling (HTSS) problem is a very important problem in optimization
operation of power systems where hydropower plants and thermal plants are accounting for a high rate of all
power sources in exiting power systems [1]. In general, hydropower plants use water in river to drive turbines
and run generators for producing electricity to loads while thermal power plants must employ fossil fuel such
as gas, oil and coal to drive gas turbines or steam turbines for generating electricity. Water can be exhausted
and full in rivers dependent on weather, namely rain and sun in seasons [2]. On the contrary, fossil fuels
cannot be recovered after using. As a result, cost of generating electricity or price of fossil fuels in thermal
power plants is a significant issue but cost of water in hydropower plants is normally ignored. Main issues
regarding hydropower plants are hydraulic constraints such as discharge limit, spillage, flood, and reservoir
limits. So, in hydropower system scheduling problem, the most difficulty issue is to solve the hydraulic
constraints successfully while the main target is to reduce cost of generating electricity in thermal power
plants [3].
Basically, hydrothermal system scheduling problem can be divided into short-term [1-10],
medium-term [11-15] and long-term models [16-20] based on the time period of scheduled optimization.
Short-term HTSS problem is classified into fixed-head model [1-7] and variable head model [8-10], and this
problem was also the most attracted problem among three different time period types. The main difference of
the problems is scheduled time period. Short-term HTSS problem considers one day to one week while
long-term HTSS considers over one year with twelve months or four seasons. The time from one week to one
month or from one month to one season is taken into account in medium-term HTSS problem. The three
problems have the same characteristic that is to consider cost of producing electricity in thermal power plants
as an objective and neglect cost in hydropower plants. In addition, renewable energies like solar energy and
wind energy are not considered in the problem.
In recent years, wind turbines have been considered in conventional power systems with
hydrothermal plants. The optimal generation between these thermal plants and these wind turbines was
successfully solved by using metaheuristic algorithms like bee colony algorithm (BCA) [21] and Wait-See
algorithm (WSA) [22]. Then, the integrated system was expanded by adding hydropower plants and
the optimal generation of the wind-hydro-thermal system were solved by using nondominated sorting genetic
algorithm-III (NSGA-III) [23], multi-objective bee colony optimization algorithm (MOBCOA) [24],
two-stage stochastic method (TSSM) [25] and sine-cosine algorithm (SCA) [26]. In [23], multi objective
functions including fuel cost and power loss are considered in which power generation of wind farms is
considered as a control variable of the combined system. In [24], uncertainty of wind speed was considered
by considering Weibull distribution function. In the study, wind turbines are calculated three cost, direction
cost, reserve cost and penalty cost. In [25-26], cascaded hydropower plants are considered together with
the power generation of thermal power plants and wind turbines. Similar to [24], the two studies also
considered the Weibull function and three costs of wind turbines. In general, almost all studies applied
metaheuristics and mainly focused on the highly successful constraint handling ability of rather than reaching
the best solutions for the problem. In addition, power loss of the system due to the impact of resistance and
reactance of conductors was not considered in these studies. This is also understood because these studies
were first application of methods for solving the new problem.
In this paper, short-term HTSS problem with fixed-head model is expanded by adding wind turbines
and considering operation range of them. On the contrary to other studies, all constraints of hydropower
plants are taken into account including discharge limit, available water and generator limits. Thermal power
plants are not constrained by available fossil fuel quantity but generators. Wind turbines are constrained
by capacity and operation wind speeds. The main purpose is to calculate cost of thermal power plants
and determine the most optimal generation for reducing this cost. For reaching the optimal solutions of
the problem, we apply PSO (PSO) [27], CFIWPSO [28], SSD [29] and CSA [30].
In summary, the contributions of the paper are follows:
- Develop wind-hydrothermal system scheduling problem with short-term model
- Propose the best decision variable selection method
- Investigate performance of PSO, FCIW-PSO, SSD and CSA

Int J Elec & Comp Eng ISSN: 2088-8708 
Optimal power generation for wind-hydro-thermal system using … (Thuan Thanh Nguyen)
5125
2. FORMULATION OF OPTIMAL SCHEDULING OF WIND-HYDRO-THERMAL SYSTEM
In the section, a wind-hydrothermal system with fixed head model is in detail described by using
figure and formulas. Figure 1 shows a system with one thermal power plant, one hydropower plant and one
wind farm located at load. The objective and constraints as well as assumption of the problem are as follows:
Wind turbine
Load
~
Fuel
~
Hydropower plant
Thermal power plant
Figure 1. A typical wind-hydro-thermal system
2.1. Objective function
Total fuel cost (TFC) for generating electricity from all thermal power plants is considered as
a major part that needs to be minimized as much as possible. The objective is shown as follows:
𝑀𝑖𝑛𝑖𝑚𝑖𝑧𝑒 𝑇𝐹𝐶 = ∑ ∑ (𝑘𝑖 + 𝑚𝑖 𝑃𝑇𝑖,𝑗 + 𝑛𝑖(𝑃𝑇𝑖,𝑗)
2
)
𝑁 𝑖𝑛
𝑗=1
𝑁 𝑡𝑝
𝑖=1
(1)
In (1), we only focus on the reduction of fuel cost from thermal power plants meanwhile the electric
generation cost from hydropower plants and wind power plants is neglected. The assumption of neglecting
the electric cost from hydroelectric plant is taken from the idea that water is a nature source with very low
price whereas all power energy from wind power plants is absolutely used with the same price and the same
cost over one scheduled day.
2.2. The set of constraints
a. Constraints from power system
In power systems, the balance between the generated and consumed power must be guaranteed as
the following model:
∑ 𝑃𝑇𝑖,𝑗
𝑁 𝑡𝑝
𝑖=1
∑ 𝑃𝐻 𝑘,𝑗 + ∑ 𝑃𝑊 𝑤,𝑗
𝑁 𝑤𝑡
𝑤=1
− 𝑃𝐿𝑜𝑎𝑑,𝑗 − 𝑃𝐿𝑜𝑠𝑠,𝑗 = 0
𝑁ℎ𝑝
𝑘=1
(2)
b. Constraint from thermal plants
Power generation of thermal power plants is limited as follows:
𝑃𝑇𝑖,𝑚𝑖𝑛 ≤ 𝑃𝑇𝑖,𝑗 ≤ 𝑃𝑇𝑖,𝑚𝑎𝑥 (3)
c. Constraint from wind turbines
Basically, power generation of a wind turbine is much dependent on wind speed. The range of
generation can be seen by the following equation [25]:
𝑃𝑊𝑤 =
{
0, (𝑊𝑉 < 𝑊𝑉𝑐𝑢𝑡−𝑖𝑛 𝑎𝑛𝑑 𝑊𝑉 > 𝑊𝑉𝑐𝑢𝑡−𝑜𝑢𝑡)
𝑃𝑊 𝑤,𝑟𝑎𝑡𝑒 ×
(𝑊𝑉 − 𝑊𝑉𝑐𝑢𝑡−𝑖𝑛)
(𝑊𝑉𝑟𝑎𝑡𝑒 − 𝑊𝑉𝑐𝑢𝑡−𝑖𝑛)
, (𝑊𝑉𝑐𝑢𝑡−𝑖𝑛 ≤ 𝑊𝑉 ≤ 𝑊𝑉𝑟𝑎𝑡𝑒)
𝑃𝑊 𝑤,𝑟𝑎𝑡𝑒 (𝑊𝑉𝑟𝑎𝑡𝑒 ≤ 𝑊𝑉 ≤ 𝑊𝑉𝑐𝑢𝑡−𝑜𝑢𝑡)
(4)

 ISSN: 2088-8708
5126
So, wind turbines are also constrained by power generation as follows:
𝑃𝑊 𝑤,𝑚𝑖𝑛 ≤ 𝑃𝑊 𝑤,𝑗 ≤ 𝑃𝑊𝑤,𝑚𝑎𝑥 (5)
d. Constraints from hydropower plants:
Limits of water Discharge: Water that is discharged through a turbine must be in a predetermined
range as follows:
𝑄 𝑘,𝑚𝑖𝑛 ≤ 𝑄 𝑘,𝑗 ≤ 𝑄 𝑘,𝑚𝑎𝑥 (6)
where Qk,j is determined as follows:
𝑄 𝑘,𝑗 = 𝑋 𝑘 + 𝑌𝑘 𝑃𝐻 𝑘,𝑗 + 𝑍 𝑘(𝑃𝐻𝑘,𝑗)
2
(7)
In addition, the total water discharge over Nin intervals must be equal to available as the constraint below:
∑ 𝑄 𝑘,𝑗
𝑁 𝑖𝑛
𝑗=1
= 𝑊𝑎𝑣𝑎𝑖,𝑘 (8)
e. Constraint of generators: Hydro generation is constrained by.
𝑃𝐻 𝑘,𝑚𝑖𝑛 ≤ 𝑃𝐻𝑘,𝑗 ≤ 𝑃𝐻𝑘,𝑚𝑎𝑥 (9)
3. CUCKOO SEARCH ALGORITHM
3.1. New solution generation mechanism
On the contrary to PSO and SSD, CSA performs two mechanisms to produce new solutions.
For each mechanism, the whole population is newly updated. So, total new solutions generated by CSA is
two times that of PSO and SSD. Lévy flights is applied in the first mechanism while mutation operation is
employed in the second one. The two mechanisms are mathematically formulated as follows:
   0
new
x x x BestSol Sol Sol Sol L     (10)
 1 1 2 2xnew
x
x
Sol rd Sol Sol if rd Pro
Sol
Sol otherwise
   
 

(11)
where α0 is a positive scaling factor; L (β) is Lévy distribution function [10]; and SolBest is the so-far best
solution among the current population; rd1 and rd2 are random numbers in the range between 0 and 1; Pro is
old solution replacement probability, which is selected within 0 and 1. Sol1 and Sol2 are two randomly
selected solutions.
3.2. Promising solution selection mechanism
This mechanism is applied to performance comparison of quality between the new xth solution and
the old xth solution to retain a better solution and abandon a worse one. So, fitness function must be
calculated for each old and new solution. Then, the following model is applied.
new
x x x
x new
x
Sol if Fit Fit
Sol
Sol Otherwise
 
 

(12)
4. RESULTS NUMERICAL RESULTS
In this section, the effectiveness of CSA is compared to that of PSO, CFIW-PSO and SSD on
the system with one thermal power plant, one hydropower plant and one wind power plant. The system is
scheduled over twenty-four one-hour intervals. The hydrothermal systems and loss coefficients are taken

5127
from Table A1 in page 284 [10] while the wind farm data is taken from wind farm 1 in Table 6 in page
760 [31]. The whole data and loss coefficients are shown in Table A1, Table A2 and Table A3 in Appendix.
The four methods are coded on Matlab program language and a computer with CPU of Intel Core
i7-2.4GHz-RAM 4GB for obtaining 50 successful runs.
In order to run these methods, population size (PS) and the maximum iteration (MI) are set to 20 and
2000 for CSA, 40 and 2000 for PSO, CFIW-PSO and SSD. The results from 50 successful runs are
summarized in Table 1 in addition to saving cost and improvement shown in Figures 2 and 3. In the two
figures, saving cost and the corresponding improvement level of CSA as compared to PSO, CFIW-PSO and
SSD are shown. So, there is no bar to show the result of CSA in the two figures. From the figures, it can
indicate that as compared to other methods CSA can reach very high reduction of minimum cost with
$6029.58, mean cost with $7576.37 and maximum cost with $9305.77 the reduction cost of CSA is
corresponding to the improvement level of 8%, 0.94% and 2.1% over PSO, CFIW-PSO and SSD. Similarly,
the mean cost and the highest cost of CSA are also much less than other methods. The improvement level of
mean cost and the highest cost can be up to 4% and 9.8%.
Table 1. Summary of results
PSO CFIW-PSO SSD CSA
Minimum cost ($) 75789.64 70420.13 71236.93 69760.06
Average cost ($) 77362.83 72718.63 73327.8 69786.46
Maximum cost ($) 79306.06 75847.33 77212.04 70000.29
Standard deviation ($) 729.5481 1438.077 1366.289 41.4461
Success rate (%) 848/50 86/50 107/50 50/50
Figure 2. Saving cost of CSA as compared to PSO, CFIW-PSO and SSD
Figure 3. Improvement of CSA over PSO, CFIW-PSO and SSD
6029,58
7576,37
9305,77
688,102
660,07
2932,17
5847,04
1396,6309
1476,87
3541,34
7211,75
1324,8429
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Minimum cost
($)
Average cost
($)
Maximum cost
($)
Standard
deviation ($)
SavingCostofCSA($)
PSO
CFIW-PSO
SSD
8,0 9,8 11,7
94,3
0,9 4,0 7,7
97,1
2,1 4,8 9,3
97,0
0,0
20,0
40,0
60,0
80,0
100,0
120,0
Minimum cost (%) Average cost (%) Maximum cost (%) Standard deviation
(%)
Improvemetn(%)
PSO CFIW-PSO SSD

 ISSN: 2088-8708
5128
In addition, the best run, the mean run, the worst run and the cost of 50 runs can be observed from
Figures 4-7. The figures indicate that CSA is always the best method with the fastest speed and all better
runs. Consequently, it leads to a conclusion that CSA is the best method for the first system. Optimal power
generation obtained by CSA is shown in Figure 8.
Figure 4. The best convergence characteristic of
four applied methods
Figure 5. The mean convergence characteristic over
50 successful runs of four applied methods
Figure 6. The worst convergence characteristic of
Figure 7. Fuel cost of 50 successful runs obtained by
Figure 8. Optimal power generation obtained by CSA
5. CONCLUSION
In this paper, four applied methods including CSA, PSO, FCIW-PSO and SSD have been applied
for solving combined wind turbine and hydrothermal systems. The four method have been implemented for
reaching 50 successful runs for comparisons. Numerical results including the best cost, mean cost and
maximum cost in addition to graphical results including convergence characteristics have been analyzed for
evaluating performance of these methods. CSA was superior to three other ones in finding the best solution,
reach very high success rate and faster speed. So, it can be concluded that CSA is a very efficient method for
determining optimal parameters of combined wind turbines and hydrothermal systems.

5129
APPENDIX
Table A1. Data of thermal power plant
k1 m1 n1 PT1,min (MW) PT1,max (MW)
373.7 9.606 0.001991 0 505
Table A2. Data of hydroelectric plant
X1 Y1 Z1 Wavai,1 PH1,min (MW) PH1,max (MW)
61.53 -0.009079 0.0007749 2559.6 0 300
The loss coefficient matrix of the system







0.000150.00001
0.000010.00005
B
Table A3. Load and wind power over 24 one-hour intervals
j 𝑃𝐿𝑜𝑎𝑑,𝑗 𝑃𝑊1,𝑗 j 𝑃𝐿𝑜𝑎𝑑,𝑗 𝑃𝑊1,𝑗 j 𝑃𝐿𝑜𝑎𝑑,𝑗 𝑃𝑊1,𝑗
1 455 99 9 665 94.8 17 721 105
2 425 108 10 675 86.4 18 740 91.2
3 415 93 11 695 120 19 700 78
4 407 82.8 12 705 99 20 678 82.8
5 400 90 13 580 111.6 21 630 114
6 420 106.8 14 605 109.2 22 585 120
7 487 81.6 15 616 111 23 540 92.4
8 604 93 16 653 81 24 503 96
REFERENCES
[1] A. J. Wood, B. F. Wollenberg, G. B. Sheblé, “Power generation, operation, and control,” John Wiley & Sons, 2013.
[2] I. A. Farhat, M. E. El-Hawary, “Fixed-head hydro-thermal scheduling using a modified bacterial foraging
algorithm,” In 2010 IEEE Electrical Power & Energy Conference IEEE, pp. 1-6, Aug. 2010.
[3] J. Sasikala, and M. Ramaswamy, “Optimal gamma based fixed head hydrothermal scheduling using genetic
algorithm,” Expert Systems with Applications, vol. 37, no. 4, pp. 3352-3357, 2010.
[4] M. Y. Chang and W. J. A. Huang, “A Practical Case Report on the Node Point of a Butterfly Model Circular
Economy: Synthesis of a New Hybrid Mineral–Hydrothermal Fertilizer for Rice Cropping,” Sustainability, vol. 12,
no. 3, pp. 1-8, 2020.
[5] N. Hamzah, K. Tokimatsu, and K. Yoshikawa, “Solid fuel from oil palm biomass residues and municipal solid
waste by hydrothermal treatment for electrical power generation in Malaysia: a review,” Sustainability, vol. 11,
no. 4, pp. 1-23, 2019.
[6] S. Ghosh, M. Kaur, S. Bhullar, and V. Karar, “Hybrid abc-bat for solving short-term hydrothermal scheduling
problems,” Energies, vol. 12, no. 3, pp. 1-15, 2019.
[7] T. T. Nguyen, N. Vu Quynh, M. Q. Duong, L. Van Dai, “Modified differential evolution algorithm: A novel
approach to optimize the operation of hydrothermal power systems while considering the different constraints and
valve point loading effects,” Energies, vol. 11, no. 3, pp. 1-30, 2018.
[8] S. K. Damodaran, T. K. Sunil Kumar, “Hydro-thermal-wind generation scheduling considering economic and
environmental factors using heuristic algorithms,” Energies, vol 11, no. 2, pp. 1-19, 2018.
[9] T. T. Nguyen, B. H. Dinh, N. V. Quynh, M. Q. Duong, M. Q, L. V. Dai, “A novel algorithm for optimal operation
of hydrothermal power systems under considering the constraints in transmission networks,” Energies, vol. 11,
no. 1, pp. 1-21, 2018.
[10] T. T. Nguyen, D. N. Vo, and A. V. Truong, “Cuckoo search algorithm for short-term hydrothermal scheduling,”
Applied Energy, vol. 132, no. 1, pp. 276-287, 2014.
[11] R. Fuentes-Loyola, V. H. Quintana, “Medium-term hydrothermal coordination by semidefinite programming,”
IEEE Transactions on Power Systems, vol. 18, no. 4, pp. 1515-1522, 2013.
[12] V. H. Ferreira, and G. H. C. Silva, “Natural optimization applied to medium-term hydrothermal coordination,”
In 2011 16th International Conference on Intelligent System Applications to Power Systems, pp. 1-6, Sep. 2011.
[13] K. I. Ourani, C. G. Baslis, and A. G. Bakirtzis, “A Stochastic Dual Dynamic Programming model for medium-term
hydrothermal scheduling in Greece,” In 2012 47th International Universities Power Engineering Conference
(UPEC), pp. 1-6, Sep. 2012.
[14] F. Beltrán, W. de Oliveira, E. C. Finardi, “Application of scenario tree reduction via quadratic process to medium-
term hydrothermal scheduling problem,” IEEE Transactions on Power Systems, pp. 1-10, 2017.

 ISSN: 2088-8708
5130
[15] M. I. Ennes, A. L. Diniz, “An efficient equivalent thermal cost function model for nonlinear mid-term hydrothermal
generation planning,” International Journal of Electrical Power & Energy Systems, vol. 63, pp. 705-712, 2014.
[16] R. W. Ferrero, J. F. Rivera, and S. M. Shahidehpour, “A dynamic programming two-stage algorithm for
long-term hydrothermal scheduling of multireservoir systems,” IEEE Transactions on Power Systems, vol. 13,
no. 4, pp. 1534-1540, 1998.
[17] L. Martinez, S. Soares, “Comparison between closed-loop and partial open-loop feedback control policies in long
term hydrothermal scheduling,” IEEE Transactions on Power Systems, vol 17, no. 2, pp. 330-336, 2002.
[18] T. Homem-de-Mello, V. L. De Matos, and E. C. Finardi, “Sampling strategies and stopping criteria for
stochastic dual dynamic programming: a case study in long-term hydrothermal scheduling,” Energy Systems, vol. 2,
pp. 1-31, 2011.
[19] G. Da Cruz, and S. Soares, “Non-uniform composite representation hydroelectric systems for long-term
hydrothermal scheduling,” IEEE Transactions on Power Systems, vol. 11, no. 2, pp. 702-707, 1996.
[20] M. Zambelli, T. G. Siqueira, M. Cicogna, and S. Soares, “Deterministic versus stochastic models for long term
hydrothermal scheduling,” In 2006 IEEE Power Engineering Society General Meeting, pp. 7, Jun. 2006.
[21] H. T. Jadhav, and R. Roy, “Gbest guided artificial bee colony algorithm for environmental/economic dispatch
considering wind power,” Expert Systems with Applications, vol. 40, no. 16, pp. 6385-6399, 2013.
[22] X. Liu, “Economic load dispatch constrained by wind power availability: A wait-and-see approach,” IEEE
Transactions on Smart Grid, vol. 1, no. 3, pp. 347-355, 2010.
[23] X. Yuan, H. Tian, Y. Yuan, Y. Huang, R. M. Ikram, “An extended NSGA-III for solution multi-objective
hydro-thermal-wind scheduling considering wind power cost,” Energy Conversion and Management, vol. 96,
pp. 568-578, 2015.
[24] J. Zhou, P. Lu, Y. Li, C. Wang, L. Yuan, and L. Mo, “Short-term hydro-thermal-wind complementary scheduling
considering uncertainty of wind power using an enhanced multi-objective bee colony optimization algorithm,”
Energy conversion and management, vol. 123, pp. 116-129, 2016.
[25] M. Daneshvar, B. Mohammadi-Ivatloo, K. Zare, S. Asadi, “Two-stage stochastic programming model for optimal
scheduling of the wind-thermal-hydropower-pumped storage system considering the flexibility assessment,”
Energy, vol. 193, 116657, 2020.
[26] K. Dasgupta, P. K. Roy, and V. Mukherjee, “Power flow based hydro-thermal-wind scheduling of hybrid power
system using sine cosine algorithm,” Electric Power Systems Research, vol. 178, 106018, 2020.
[27] T. T. Nguyen, and N. D. Vo, “Improved particle swarm optimization for combined heat and power economic
dispatch,” Scientia Iranica, vol. 23, no. 3, pp. 1318-1334, 2016.
[28] L. C. Kien, T. L. Duong, V. D. Phan, and T. T. Nguyen, “Maximizing Total Profit of Thermal Generation Units in
Competitive Electric Market by Using a Proposed Particle Swarm Optimization,” Sustainability, vol. 12, no. 3,
pp. 1265, 2020.
[29] A. Tharwat, T. Gabel, “Parameters optimization of support vector machines for imbalanced data using social ski
driver algorithm,” Neural Computing and Applications, pp. 1-14, 2019.
[30] X. Meng, J. Chang, X. Wang, and Y. Wang, “Multi-objective hydropower station operation using an improved
cuckoo search algorithm,” Energy, vol. 168, pp. 425-439, 2019.
[31] H. Zhang, D. Yue, X. Xie, C. Dou, F Sun, “Gradient decent based multi-objective cultural differential evolution for
short-term hydrothermal optimal scheduling of economic emission with integrating wind power and photovoltaic
power,” Energy, vol. 122, pp. 748-766, 2017.

Optimal power generation for wind-hydro-thermal system using meta-heuristic algorithms

More Related Content

What's hot (20)

Similar to Optimal power generation for wind-hydro-thermal system using meta-heuristic algorithms (20)

More from IJECEIAES (20)

Recently uploaded (20)

Optimal power generation for wind-hydro-thermal system using meta-heuristic algorithms