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A novel differential evolution algorithm for economic power dispatch problem
Department of Electronics and Communication, University of Allahabad, Prayagraj, India |
In power systems, Economic Power dispatch Problem (EPP) is an influential optimization problem which is a highly non-convex and non-linear optimization problem. In the current study, a novel version of Differential Evolution (NDE) is used to solve this particular problem. NDE algorithm enhances local and global search capability along with efficient utilization of time and space by making use of two elite features: selfadaptive control parameter and single population structure. The combined effect of these concepts improves the performance of Differential Evolution (DE) without compromising on quality of the solution and balances the exploitation and exploration capabilities of DE. The efficiency of NDE is validated by evaluating on three benchmark cases of the power system problem having constraints such as power balance and power generation along with nonsmooth cost function and is compared with other optimization algorithms. The Numerical outcomes uncovered that NDE performed well for all the benchmark cases and maintained a trade-off between convergence rate and efficiency.
References:
[1] |
O. Abedinia, N. Amjady, A. Ghasemi and Z. Hejrati,
Solution of economic load dispatch problem via hybrid particle swarm optimization with time–varying acceleration coefficients and bacteria foraging algorithm techniques, International Transactions on Electrical Energy Systems, 23(8) (2012), 1504-1522.
|
[2] |
B. R. Adarsh, T. Raghunathan, T. Jayabarathi and X. S. Yang,
Economic dispatch using chaotic bat algorithm, Energy, 96 (2016), 666-675.
|
[3] |
W. M. Ali and H. Z. Sabry,
Constrained optimization based on modified differential evolution algorithm, Information Sciences, 194 (2012), 171-208.
|
[4] |
B. V. Babu and R. Angira,
Modified differential evolution (MDE) for optimization of nonlinear chemical processes, Comput. Chem. Engin, 30 (2006), 989-1002.
|
[5] |
A. Bhattacharya and P. K. Chattopadhyay,
Solving complex economic load dispatch problems using biogeography–based optimization, Expert Systems with Applications, 37 (2010a), 3605-3615.
|
[6] |
A. Biswas, S. Dasgupta, B. K. Panigrahi, V. R. Pandi, S. Das, A. Abraham and Y. Badr,
Economic load dispatch using a chemotactic differential evolution algorithm, 4th International Conference on Hybrid Artificial Intelligent Systems, LNAI, 5572 (2009), 252-260.
|
[7] |
J. Brest, V. Zumer and M. S. Maucecc, Control parameters in self-adaptive differential evolution, Bioinspired Optimization Methods and Their Applications, (2006), 35–44. |
[8] |
K. T. Chaturvedi, M. Pandit and L. Srivastava,
Self–organizing hierarchical particle swarm optimization for nonconvex economic dispatch, IEEE Transactions on Power Systems, 23 (2008), 1079-1087.
|
[9] |
B. H. Choudhary and S. Rahman,
A review of recent advances in economic dispatch, IEEE Trans. on Power System, 5 (1990), 1248-1259.
|
[10] |
S. Elsayed, M. F. Zaman and R. Sarker,
Automated differential evolution for solving dynamic economic dispatch problems, Intelligent and Evolutionary Systems, Proceedings in Adaptation, Learning and Optimization, 5 (2016), 357-369.
|
[11] |
Z. L. Gaing,
Particle swarm optimization to solving the economic dispatch considering generator constraints, IEEE Trans. on Power Systems, 18 (2003), 1187-1195.
|
[12] |
A. Goli, H. K. Zareh, R. Tavakkoli–Moghaddam and A. Sadeghieh,
Application of robust optimization for a product portfolio problem using an invasive weed optimization algorithm, Numerical Algebra, Control & Optimization, 9 (2019), 187-209.
doi: 10.3934/naco.2019014. |
[13] |
A. Goli, H. Khademi–Zare, R. Tavakkoli–Moghaddam, A. Sadeghieh, M. Sasanian and R. M. Kordestanizadeh,
An integrated approach based on artificial intelligence and novel meta–heuristic algorithms to predict demand for dairy products: a case study, Network: Computation in Neural Systems, 32 (2021), 1-35.
|
[14] |
A. Goli, H. K. Zareh, R. Tavakkoli–Moghaddam and A. Sadeghieh,
A comprehensive model of demand prediction based on hybrid artificial intelligence and metaheuristic algorithms: A case study in dairy industry, Journal of Industrial and Systems Engineering, 11 (2018), 190-203.
|
[15] |
D. He, F. Wang and Z. Mao,
A hybrid genetic algorithm approach based on differential evolution for economic dispatch with valve-point effect, International Journal of Electrical Power and Energy Systems, 30 (2008), 31-38.
|
[16] |
N. T. Hung, N. Hung, P. D. V. Nguyen and D. T. Viet, Application of improved differential evolution algorithm for economic and emission dispatch of thermal power generation plants, Proceedings of the 3rd International Conference on Machine Learning and Soft Computing, (2019), 93–98. |
[17] |
J. O. Kim, D. J. Shin, J. N. Park and C. Singh,
Atavistic genetic algorithm for economic dispatch with valve point effect, Electric Power Systems Research, 62 (2002), 201-207.
|
[18] |
P. Kumar, M. Pant and V. P. Singh,
Two self adaptive variants of differential evolution algorithm for global optimization, Int. J. of Appl. Math. and Mech., 8 (2012), 22-34.
|
[19] |
J. Liu and J. Lampinen,
A fuzzy adaptive differential evolution algorithm, Soft Computing, 9 (2005), 448-462.
|
[20] |
R. Lotfi, N. Mardani and G. W. Weber,
Robust bi–level programming for renewable energy location, International Journal of Energy Research, 45 (2021), 7521-7534.
|
[21] |
R. Lotfi, Z. Yadegari, S. H. Hosseini, A. H. Khameneh, E. B. Tirkolaee and G. W. Weber, A robust time–cost–quality–energy–environment trade–off with resource–constrained in project management: A case study for a bridge construction project, Journal of Industrial & Management Optimization, 13 (2020). |
[22] |
R. Lotfi, Y. Z. Mehrjerdi, M. S. Pishvaee, A. Sadeghieh and G. W. Weber,
A robust optimization model for sustainable and resilient closed–loop supply chain network design considering conditional value at risk, Numerical Algebra, Control & Optimization, 11 (2021), 221-253.
doi: 10.3934/naco.2020023. |
[23] |
E. Mezura–Montes, M. E. Miranda-Varela and R. C. Gómez-Ramón,
Differential evolution in constrained numerical optimization: an empirical study, Information Sciences, 180 (2010), 4223-4262.
doi: 10.1016/j.ins.2010.07.023. |
[24] |
V. R. Pandi, B. K. Panigrahi, A. Mohapatra and M. K. Mallick,
Economic load dispatch solution by improved harmony search with wavelet mutation, International Journal of Computational Science and Engineering, 6 (2011), 122-131.
|
[25] |
L. Ping, J. Sun and Q. Chen,
Solving Power economic dispatch problem with a novel quantum–behaved particle swarm optimization algorithm, Mathematical Problems in Engineering, 2020 (2020), 1-11.
|
[26] |
Po oja, P. Chaturvedi, P. Kumar and A. Tomar,
A novel differential evolution approach for constraint optimisation, Int. J. Bio–Inspired Computation, 12 (2018), 254-265.
|
[27] |
B. Y. Qu, Y. S. Zhu, Y. C. Jiao, M. Y. Wu, P. N. Suganthan and J. J. Liang,
A survey on multi–objective evolutionary algorithms for the solution of the environmental/economic dispatch problems, Swarm and Evolutionary Computation, 38 (2018), 1-11.
|
[28] |
R. Rahmani, M. F. Othman, R. Yusof and M. Khalid,
Solving economic dispatch problem using particle swarm optimization by an evolutionary technique for initializing particles, Journal of Theoretical and Applied Information Technology, 46 (2012), 526-536.
|
[29] |
A. Safari and H. Shayeghi,
Iteration particle swarm optimization procedure for economic load dispatch with generator constraints, Expert Systems with Applications, 38 (2011), 6043-6048.
|
[30] |
N. Sinha, R. Chakrabarti and P. K. Chattopadhyay,
Evolutionary programming techniques for economic load dispatch, IEEE Trans. Evol. Comput., 7 (2003), 83-94.
|
[31] |
R. Storn and K. Price,
Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization, 11 (1997), 341-359.
doi: 10.1023/A:1008202821328. |
[32] |
M. F. Zaman, S. M. Elsayed, T. Ray and R. A. Sarker,
Evolutionary algorithms for dynamic economic dispatch problems, IEEE Transactions on Power Systems, 31 (2016), 1486-1495.
|
show all references
References:
[1] |
O. Abedinia, N. Amjady, A. Ghasemi and Z. Hejrati,
Solution of economic load dispatch problem via hybrid particle swarm optimization with time–varying acceleration coefficients and bacteria foraging algorithm techniques, International Transactions on Electrical Energy Systems, 23(8) (2012), 1504-1522.
|
[2] |
B. R. Adarsh, T. Raghunathan, T. Jayabarathi and X. S. Yang,
Economic dispatch using chaotic bat algorithm, Energy, 96 (2016), 666-675.
|
[3] |
W. M. Ali and H. Z. Sabry,
Constrained optimization based on modified differential evolution algorithm, Information Sciences, 194 (2012), 171-208.
|
[4] |
B. V. Babu and R. Angira,
Modified differential evolution (MDE) for optimization of nonlinear chemical processes, Comput. Chem. Engin, 30 (2006), 989-1002.
|
[5] |
A. Bhattacharya and P. K. Chattopadhyay,
Solving complex economic load dispatch problems using biogeography–based optimization, Expert Systems with Applications, 37 (2010a), 3605-3615.
|
[6] |
A. Biswas, S. Dasgupta, B. K. Panigrahi, V. R. Pandi, S. Das, A. Abraham and Y. Badr,
Economic load dispatch using a chemotactic differential evolution algorithm, 4th International Conference on Hybrid Artificial Intelligent Systems, LNAI, 5572 (2009), 252-260.
|
[7] |
J. Brest, V. Zumer and M. S. Maucecc, Control parameters in self-adaptive differential evolution, Bioinspired Optimization Methods and Their Applications, (2006), 35–44. |
[8] |
K. T. Chaturvedi, M. Pandit and L. Srivastava,
Self–organizing hierarchical particle swarm optimization for nonconvex economic dispatch, IEEE Transactions on Power Systems, 23 (2008), 1079-1087.
|
[9] |
B. H. Choudhary and S. Rahman,
A review of recent advances in economic dispatch, IEEE Trans. on Power System, 5 (1990), 1248-1259.
|
[10] |
S. Elsayed, M. F. Zaman and R. Sarker,
Automated differential evolution for solving dynamic economic dispatch problems, Intelligent and Evolutionary Systems, Proceedings in Adaptation, Learning and Optimization, 5 (2016), 357-369.
|
[11] |
Z. L. Gaing,
Particle swarm optimization to solving the economic dispatch considering generator constraints, IEEE Trans. on Power Systems, 18 (2003), 1187-1195.
|
[12] |
A. Goli, H. K. Zareh, R. Tavakkoli–Moghaddam and A. Sadeghieh,
Application of robust optimization for a product portfolio problem using an invasive weed optimization algorithm, Numerical Algebra, Control & Optimization, 9 (2019), 187-209.
doi: 10.3934/naco.2019014. |
[13] |
A. Goli, H. Khademi–Zare, R. Tavakkoli–Moghaddam, A. Sadeghieh, M. Sasanian and R. M. Kordestanizadeh,
An integrated approach based on artificial intelligence and novel meta–heuristic algorithms to predict demand for dairy products: a case study, Network: Computation in Neural Systems, 32 (2021), 1-35.
|
[14] |
A. Goli, H. K. Zareh, R. Tavakkoli–Moghaddam and A. Sadeghieh,
A comprehensive model of demand prediction based on hybrid artificial intelligence and metaheuristic algorithms: A case study in dairy industry, Journal of Industrial and Systems Engineering, 11 (2018), 190-203.
|
[15] |
D. He, F. Wang and Z. Mao,
A hybrid genetic algorithm approach based on differential evolution for economic dispatch with valve-point effect, International Journal of Electrical Power and Energy Systems, 30 (2008), 31-38.
|
[16] |
N. T. Hung, N. Hung, P. D. V. Nguyen and D. T. Viet, Application of improved differential evolution algorithm for economic and emission dispatch of thermal power generation plants, Proceedings of the 3rd International Conference on Machine Learning and Soft Computing, (2019), 93–98. |
[17] |
J. O. Kim, D. J. Shin, J. N. Park and C. Singh,
Atavistic genetic algorithm for economic dispatch with valve point effect, Electric Power Systems Research, 62 (2002), 201-207.
|
[18] |
P. Kumar, M. Pant and V. P. Singh,
Two self adaptive variants of differential evolution algorithm for global optimization, Int. J. of Appl. Math. and Mech., 8 (2012), 22-34.
|
[19] |
J. Liu and J. Lampinen,
A fuzzy adaptive differential evolution algorithm, Soft Computing, 9 (2005), 448-462.
|
[20] |
R. Lotfi, N. Mardani and G. W. Weber,
Robust bi–level programming for renewable energy location, International Journal of Energy Research, 45 (2021), 7521-7534.
|
[21] |
R. Lotfi, Z. Yadegari, S. H. Hosseini, A. H. Khameneh, E. B. Tirkolaee and G. W. Weber, A robust time–cost–quality–energy–environment trade–off with resource–constrained in project management: A case study for a bridge construction project, Journal of Industrial & Management Optimization, 13 (2020). |
[22] |
R. Lotfi, Y. Z. Mehrjerdi, M. S. Pishvaee, A. Sadeghieh and G. W. Weber,
A robust optimization model for sustainable and resilient closed–loop supply chain network design considering conditional value at risk, Numerical Algebra, Control & Optimization, 11 (2021), 221-253.
doi: 10.3934/naco.2020023. |
[23] |
E. Mezura–Montes, M. E. Miranda-Varela and R. C. Gómez-Ramón,
Differential evolution in constrained numerical optimization: an empirical study, Information Sciences, 180 (2010), 4223-4262.
doi: 10.1016/j.ins.2010.07.023. |
[24] |
V. R. Pandi, B. K. Panigrahi, A. Mohapatra and M. K. Mallick,
Economic load dispatch solution by improved harmony search with wavelet mutation, International Journal of Computational Science and Engineering, 6 (2011), 122-131.
|
[25] |
L. Ping, J. Sun and Q. Chen,
Solving Power economic dispatch problem with a novel quantum–behaved particle swarm optimization algorithm, Mathematical Problems in Engineering, 2020 (2020), 1-11.
|
[26] |
Po oja, P. Chaturvedi, P. Kumar and A. Tomar,
A novel differential evolution approach for constraint optimisation, Int. J. Bio–Inspired Computation, 12 (2018), 254-265.
|
[27] |
B. Y. Qu, Y. S. Zhu, Y. C. Jiao, M. Y. Wu, P. N. Suganthan and J. J. Liang,
A survey on multi–objective evolutionary algorithms for the solution of the environmental/economic dispatch problems, Swarm and Evolutionary Computation, 38 (2018), 1-11.
|
[28] |
R. Rahmani, M. F. Othman, R. Yusof and M. Khalid,
Solving economic dispatch problem using particle swarm optimization by an evolutionary technique for initializing particles, Journal of Theoretical and Applied Information Technology, 46 (2012), 526-536.
|
[29] |
A. Safari and H. Shayeghi,
Iteration particle swarm optimization procedure for economic load dispatch with generator constraints, Expert Systems with Applications, 38 (2011), 6043-6048.
|
[30] |
N. Sinha, R. Chakrabarti and P. K. Chattopadhyay,
Evolutionary programming techniques for economic load dispatch, IEEE Trans. Evol. Comput., 7 (2003), 83-94.
|
[31] |
R. Storn and K. Price,
Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization, 11 (1997), 341-359.
doi: 10.1023/A:1008202821328. |
[32] |
M. F. Zaman, S. M. Elsayed, T. Ray and R. A. Sarker,
Evolutionary algorithms for dynamic economic dispatch problems, IEEE Transactions on Power Systems, 31 (2016), 1486-1495.
|

Reference | Problem | Algorithms | Case Study |
[17] | ELD with valve point effect | GA | 13 generating units |
[30] | ELD | EP | 3, 13, and 40 generating units |
[11] | ELD with generator constraints | PSO | 6, 15, and 40 generating units |
[15] | ELD with valve-point effect | HGA | 13 and 40 generating units |
[8] | ELD | SPSO | 6, 15 and 40 generating units |
[6] | ELD | CDE | 6 and 13 generating units |
[5] | ELD | BBO | 6, 10, 20 and 40 generating units |
[29] | ELD with generator constraints | IPSO | 6 and 15 generating units |
[24] | ELD | IHSWM | 40 generating units |
[28] | ELD | MPSO | 3, 6, 15, and 40 generation units |
[1] | ELD | HPSOTVAC | 6, 15 and 38 generating units |
[10] | Dynamic ELD | ADE | (1) 5-unit thermal system with Ploss for a 24-hours planning horizon; (2) 10-unit thermal system without Ploss for a 12-hours planning horizon; (3) 10-unit thermal system without Ploss for a 24-hours planning horizon |
[2] | ELD | CBA | 6, 13, 20, 40 and 160 generating units |
[32] | Dynamic ELD | EA | (1) 5-unit thermal problems with and without Ploss (2) 10-unit thermal problems with and without Ploss; (3) 7-unit hydro-thermal problem without Ploss; (4) 19-unit solar-Cthermal system without Ploss; (5) 6-unit wind-Cthermal system with Ploss |
[27] | Environmental/ Economic Dispatch | MOEA | (1) 6-generator 30-bus standard test system; (2) 13-generator 57-bus system; (3) 3-generator system; (4) 6-generator system; (5) 14-generator 118-bus system; (6) 40-generator system; (7) 10-generator system |
[16] | Economic and Emission Dispatch | IDE | 6 generating units |
[25] | ELD | QPSO | 6, 15 and 40 generating units |
[20] | Renewable Energy Location | Robust Bi-Level Programming | Locating renewable energy sites |
This research | EPP | NDE | 6, 15 and 40 generating units |
ELD: Economic Load Dispatch EPP:EconomicPower dispatch GA: Genetic Algorithm EP: Evolutionary Programming PSO: Particle Swarm Optimization HGA: Hybrid Genetic Algorithm approach based on Differential Evolution SPSO: Self-organizing Hierarchical Particle Swarm Optimization CDE: Chemotactic Differential Evolution Algorithm BBO: Biogeography-Based Optimization IPSO: Iteration Particle Swarm Optimization IHSWM: Improved Harmony Search with Wavelet Mutation MPSO: Particle Swarm Optimization by Evolutionary Technique HPSOTVAC: Hybrid Particle Swarm Optimization with Time-Varying Acceleration Coefficients ADE: Automated Differential Evolution CBA: Chaotic Bat Algorithm EA: Evolutionary Algorithms MOEA: Multi-Objective Evolutionary Algorithms IDE: Improved Differential Evolution Algorithm SG-QPSO: Novel Quantum-Behaved Particle Swarm Optimization Algorithm NDE: Novel Differential Evolution |
Reference | Problem | Algorithms | Case Study |
[17] | ELD with valve point effect | GA | 13 generating units |
[30] | ELD | EP | 3, 13, and 40 generating units |
[11] | ELD with generator constraints | PSO | 6, 15, and 40 generating units |
[15] | ELD with valve-point effect | HGA | 13 and 40 generating units |
[8] | ELD | SPSO | 6, 15 and 40 generating units |
[6] | ELD | CDE | 6 and 13 generating units |
[5] | ELD | BBO | 6, 10, 20 and 40 generating units |
[29] | ELD with generator constraints | IPSO | 6 and 15 generating units |
[24] | ELD | IHSWM | 40 generating units |
[28] | ELD | MPSO | 3, 6, 15, and 40 generation units |
[1] | ELD | HPSOTVAC | 6, 15 and 38 generating units |
[10] | Dynamic ELD | ADE | (1) 5-unit thermal system with Ploss for a 24-hours planning horizon; (2) 10-unit thermal system without Ploss for a 12-hours planning horizon; (3) 10-unit thermal system without Ploss for a 24-hours planning horizon |
[2] | ELD | CBA | 6, 13, 20, 40 and 160 generating units |
[32] | Dynamic ELD | EA | (1) 5-unit thermal problems with and without Ploss (2) 10-unit thermal problems with and without Ploss; (3) 7-unit hydro-thermal problem without Ploss; (4) 19-unit solar-Cthermal system without Ploss; (5) 6-unit wind-Cthermal system with Ploss |
[27] | Environmental/ Economic Dispatch | MOEA | (1) 6-generator 30-bus standard test system; (2) 13-generator 57-bus system; (3) 3-generator system; (4) 6-generator system; (5) 14-generator 118-bus system; (6) 40-generator system; (7) 10-generator system |
[16] | Economic and Emission Dispatch | IDE | 6 generating units |
[25] | ELD | QPSO | 6, 15 and 40 generating units |
[20] | Renewable Energy Location | Robust Bi-Level Programming | Locating renewable energy sites |
This research | EPP | NDE | 6, 15 and 40 generating units |
ELD: Economic Load Dispatch EPP:EconomicPower dispatch GA: Genetic Algorithm EP: Evolutionary Programming PSO: Particle Swarm Optimization HGA: Hybrid Genetic Algorithm approach based on Differential Evolution SPSO: Self-organizing Hierarchical Particle Swarm Optimization CDE: Chemotactic Differential Evolution Algorithm BBO: Biogeography-Based Optimization IPSO: Iteration Particle Swarm Optimization IHSWM: Improved Harmony Search with Wavelet Mutation MPSO: Particle Swarm Optimization by Evolutionary Technique HPSOTVAC: Hybrid Particle Swarm Optimization with Time-Varying Acceleration Coefficients ADE: Automated Differential Evolution CBA: Chaotic Bat Algorithm EA: Evolutionary Algorithms MOEA: Multi-Objective Evolutionary Algorithms IDE: Improved Differential Evolution Algorithm SG-QPSO: Novel Quantum-Behaved Particle Swarm Optimization Algorithm NDE: Novel Differential Evolution |
Unit no. | |||||
1 | 0.007 | 7 | 240 | 100 | 500 |
2 | 0.0095 | 10 | 200 | 50 | 200 |
3 | 0.009 | 8.5 | 220 | 80 | 300 |
4 | 0.009 | 11 | 200 | 50 | 150 |
5 | 0.008 | 10.5 | 220 | 50 | 200 |
6 | 0.0075 | 12 | 190 | 50 | 120 |
Unit no. | |||||
1 | 0.007 | 7 | 240 | 100 | 500 |
2 | 0.0095 | 10 | 200 | 50 | 200 |
3 | 0.009 | 8.5 | 220 | 80 | 300 |
4 | 0.009 | 11 | 200 | 50 | 150 |
5 | 0.008 | 10.5 | 220 | 50 | 200 |
6 | 0.0075 | 12 | 190 | 50 | 120 |
Unit no. | Zone-1 | Zone-2 | |||
1 | 80 | 120 | 440 | 210-240 | 350-380 |
2 | 50 | 90 | 170 | 90-110 | 140-160 |
3 | 65 | 100 | 200 | 150-170 | 210-240 |
4 | 50 | 90 | 150 | 80-90 | 110-120 |
5 | 50 | 90 | 190 | 90-110 | 140-150 |
6 | 50 | 90 | 110 | 75-85 | 100-105 |
Unit no. | Zone-1 | Zone-2 | |||
1 | 80 | 120 | 440 | 210-240 | 350-380 |
2 | 50 | 90 | 170 | 90-110 | 140-160 |
3 | 65 | 100 | 200 | 150-170 | 210-240 |
4 | 50 | 90 | 150 | 80-90 | 110-120 |
5 | 50 | 90 | 190 | 90-110 | 140-150 |
6 | 50 | 90 | 110 | 75-85 | 100-105 |
Unit no. | NDE | DE | GA | PSO |
1 | 441.8657 | 446.7157 | 474.8066 | 447.497 |
2 | 169.6242 | 172.7829 | 178.6363 | 173.3221 |
3 | 259.2367 | 259.1119 | 262.2089 | 263.4745 |
4 | 139.5649 | 142.234 | 134.2826 | 139.0594 |
5 | 160.22 | 165.8878 | 151.9039 | 165.4761 |
6 | 105.0001 | 88.735 | 74.1812 | 87.128 |
13.0289 | 12.4673 | 13.0217 | 12.9584 | |
Total output power | 1, 276.03 | 1, 275.47 | 1, 276.03 | 1, 276.01 |
Min cost ($/hr) | 15, 444.09 | 15, 449.48 | 15, 459 | 15, 450 |
Mean cost ($/hr) | 15, 448.48 | 15, 452.28 | 15, 469 | 15, 454 |
Unit no. | NDE | DE | GA | PSO |
1 | 441.8657 | 446.7157 | 474.8066 | 447.497 |
2 | 169.6242 | 172.7829 | 178.6363 | 173.3221 |
3 | 259.2367 | 259.1119 | 262.2089 | 263.4745 |
4 | 139.5649 | 142.234 | 134.2826 | 139.0594 |
5 | 160.22 | 165.8878 | 151.9039 | 165.4761 |
6 | 105.0001 | 88.735 | 74.1812 | 87.128 |
13.0289 | 12.4673 | 13.0217 | 12.9584 | |
Total output power | 1, 276.03 | 1, 275.47 | 1, 276.03 | 1, 276.01 |
Min cost ($/hr) | 15, 444.09 | 15, 449.48 | 15, 459 | 15, 450 |
Mean cost ($/hr) | 15, 448.48 | 15, 452.28 | 15, 469 | 15, 454 |
Unit no. | |||||
1 | 0.000299 | 10.1 | 671 | 455 | 150 |
2 | 0.000183 | 10.2 | 574 | 455 | 150 |
3 | 0. 001126 | 8.8 | 374 | 130 | 20 |
4 | 0. 001126 | 8.8 | 374 | 130 | 20 |
5 | 0.000205 | 10.4 | 461 | 470 | 150 |
6 | 0.000301 | 10.1 | 630 | 460 | 135 |
7 | 0.000364 | 9.8 | 548 | 465 | 135 |
8 | 0.000338 | 11.2 | 227 | 300 | 60 |
9 | 0.000807 | 11.2 | 173 | 162 | 25 |
10 | 0. 001203 | 10.7 | 175 | 160 | 25 |
11 | 0. 003586 | 10.2 | 186 | 80 | 20 |
12 | 0. 005513 | 9.9 | 230 | 80 | 20 |
13 | 0.000371 | 13.1 | 225 | 85 | 25 |
14 | 0.001929 | 12.1 | 309 | 55 | 15 |
15 | 0.004447 | 12.4 | 323 | 55 | 15 |
Unit no. | |||||
1 | 0.000299 | 10.1 | 671 | 455 | 150 |
2 | 0.000183 | 10.2 | 574 | 455 | 150 |
3 | 0. 001126 | 8.8 | 374 | 130 | 20 |
4 | 0. 001126 | 8.8 | 374 | 130 | 20 |
5 | 0.000205 | 10.4 | 461 | 470 | 150 |
6 | 0.000301 | 10.1 | 630 | 460 | 135 |
7 | 0.000364 | 9.8 | 548 | 465 | 135 |
8 | 0.000338 | 11.2 | 227 | 300 | 60 |
9 | 0.000807 | 11.2 | 173 | 162 | 25 |
10 | 0. 001203 | 10.7 | 175 | 160 | 25 |
11 | 0. 003586 | 10.2 | 186 | 80 | 20 |
12 | 0. 005513 | 9.9 | 230 | 80 | 20 |
13 | 0.000371 | 13.1 | 225 | 85 | 25 |
14 | 0.001929 | 12.1 | 309 | 55 | 15 |
15 | 0.004447 | 12.4 | 323 | 55 | 15 |
Unit no. | Zone-1 | Zone-2 | Zone-3 | |||
1 | 180 | 120 | 400 | 150-150 | 150-150 | 150-150 |
2 | 180 | 120 | 300 | 185-255 | 305-335 | 430-450 |
3 | 130 | 130 | 105 | 20-20 | 20-20 | 20-20 |
4 | 130 | 130 | 100 | 20-20 | 20-20 | 20-20 |
5 | 80 | 120 | 90 | 180-200 | 305-335 | 390-430 |
6 | 80 | 120 | 400 | 230-255 | 335-395 | 430-455 |
7 | 80 | 120 | 350 | 135-135 | 135-135 | 135-135 |
8 | 65 | 100 | 95 | 60-60 | 60-60 | 60-60 |
9 | 60 | 100 | 105 | 60-60 | 25-25 | 25-25 |
10 | 60 | 100 | 110 | 25-25 | 25-25 | 25-25 |
11 | 80 | 80 | 60 | 20-20 | 20-20 | 20-20 |
12 | 80 | 80 | 40 | 30-40 | 55-65 | 20-20 |
13 | 80 | 80 | 30 | 25-25 | 25-25 | 25-25 |
14 | 55 | 55 | 20 | 15-15 | 15-15 | 15-15 |
15 | 55 | 55 | 20 | 15-15 | 15-15 | 15-15 |
Unit no. | Zone-1 | Zone-2 | Zone-3 | |||
1 | 180 | 120 | 400 | 150-150 | 150-150 | 150-150 |
2 | 180 | 120 | 300 | 185-255 | 305-335 | 430-450 |
3 | 130 | 130 | 105 | 20-20 | 20-20 | 20-20 |
4 | 130 | 130 | 100 | 20-20 | 20-20 | 20-20 |
5 | 80 | 120 | 90 | 180-200 | 305-335 | 390-430 |
6 | 80 | 120 | 400 | 230-255 | 335-395 | 430-455 |
7 | 80 | 120 | 350 | 135-135 | 135-135 | 135-135 |
8 | 65 | 100 | 95 | 60-60 | 60-60 | 60-60 |
9 | 60 | 100 | 105 | 60-60 | 25-25 | 25-25 |
10 | 60 | 100 | 110 | 25-25 | 25-25 | 25-25 |
11 | 80 | 80 | 60 | 20-20 | 20-20 | 20-20 |
12 | 80 | 80 | 40 | 30-40 | 55-65 | 20-20 |
13 | 80 | 80 | 30 | 25-25 | 25-25 | 25-25 |
14 | 55 | 55 | 20 | 15-15 | 15-15 | 15-15 |
15 | 55 | 55 | 20 | 15-15 | 15-15 | 15-15 |
Unit no. | NDE | DE | MPSO | GA | PSO | IPSO |
1 | 446.3316 | 454.998 | 455 | 415.31 | 455 | 455 |
2 | 366.7521 | 379.996 | 455 | 359.72 | 380 | 380 |
3 | 127.9874 | 129.9991 | 130 | 104.43 | 130 | 129.97 |
4 | 129.1781 | 129.9899 | 130 | 74.99 | 130 | 130 |
5 | 165.6126 | 169.9968 | 286.4128 | 380.28 | 170 | 169.93 |
6 | 423.1885 | 429.9944 | 460 | 426.79 | 460 | 459.88 |
7 | 415.0202 | 429.9944 | 465 | 341.32 | 430 | 429.25 |
8 | 132.9585 | 120.1228 | 60 | 124.79 | 60 | 60.43 |
9 | 124.8283 | 47.6016 | 25 | 133.14 | 71.05 | 74.78 |
10 | 82.68406 | 146.0069 | 37.5603 | 89.26 | 159.85 | 158.02 |
11 | 68.83981 | 79.99735 | 20 | 60.06 | 80 | 80 |
12 | 71.96576 | 79.9997 | 80 | 50 | 80 | 78.57 |
13 | 37.01169 | 25.0118 | 25 | 38.77 | 25 | 25 |
14 | 25.78839 | 16.8516 | 15 | 41.94 | 15 | 15 |
15 | 24.5418 | 20.6135 | 15 | 22.64 | 15 | 15 |
29.8923 | 31.1792 | 28.9734 | 38.278 | 30.908 | 30.858 | |
Total Output Power | 2, 659.89 | 2, 661.20 | 2, 658.97 | 2, 668.40 | 2, 660.90 | 2, 660.80 |
Mean Cost ($/Hr) | 32, 561.89 | 32, 747.16 | 32, 569.95 | 33, 113 | 32, 708 | 32, 709 |
Unit no. | NDE | DE | MPSO | GA | PSO | IPSO |
1 | 446.3316 | 454.998 | 455 | 415.31 | 455 | 455 |
2 | 366.7521 | 379.996 | 455 | 359.72 | 380 | 380 |
3 | 127.9874 | 129.9991 | 130 | 104.43 | 130 | 129.97 |
4 | 129.1781 | 129.9899 | 130 | 74.99 | 130 | 130 |
5 | 165.6126 | 169.9968 | 286.4128 | 380.28 | 170 | 169.93 |
6 | 423.1885 | 429.9944 | 460 | 426.79 | 460 | 459.88 |
7 | 415.0202 | 429.9944 | 465 | 341.32 | 430 | 429.25 |
8 | 132.9585 | 120.1228 | 60 | 124.79 | 60 | 60.43 |
9 | 124.8283 | 47.6016 | 25 | 133.14 | 71.05 | 74.78 |
10 | 82.68406 | 146.0069 | 37.5603 | 89.26 | 159.85 | 158.02 |
11 | 68.83981 | 79.99735 | 20 | 60.06 | 80 | 80 |
12 | 71.96576 | 79.9997 | 80 | 50 | 80 | 78.57 |
13 | 37.01169 | 25.0118 | 25 | 38.77 | 25 | 25 |
14 | 25.78839 | 16.8516 | 15 | 41.94 | 15 | 15 |
15 | 24.5418 | 20.6135 | 15 | 22.64 | 15 | 15 |
29.8923 | 31.1792 | 28.9734 | 38.278 | 30.908 | 30.858 | |
Total Output Power | 2, 659.89 | 2, 661.20 | 2, 658.97 | 2, 668.40 | 2, 660.90 | 2, 660.80 |
Mean Cost ($/Hr) | 32, 561.89 | 32, 747.16 | 32, 569.95 | 33, 113 | 32, 708 | 32, 709 |
Unit no. | |||||
1 | 0.00708 | 9.15 | 1728.3 | 114 | 36 |
2 | 0.00313 | 7.97 | 647.85 | 114 | 36 |
3 | 0.00313 | 7.95 | 649.69 | 120 | 60 |
4 | 0.00313 | 7.97 | 647.83 | 190 | 80 |
5 | 0.00313 | 7.97 | 647.81 | 97 | 47 |
6 | 0.00298 | 6.63 | 785.96 | 140 | 68 |
7 | 0.00298 | 6.63 | 785.96 | 300 | 110 |
8 | 0.00284 | 6.66 | 794.53 | 300 | 135 |
9 | 0.00284 | 6.66 | 794.53 | 300 | 135 |
10 | 0.00277 | 7.1 | 801.32 | 300 | 130 |
11 | 0.00277 | 7.1 | 801.32 | 375 | 94 |
12 | 0.52124 | 3.33 | 1055.1 | 375 | 94 |
13 | 0.52124 | 3.33 | 1055.1 | 500 | 125 |
14 | 0.52124 | 3.33 | 1055.1 | 500 | 125 |
15 | 0.0114 | 5.35 | 148.89 | 500 | 125 |
16 | 0.0016 | 6.43 | 222.92 | 500 | 125 |
17 | 0.0016 | 6.43 | 222.92 | 500 | 220 |
18 | 0.0016 | 6.43 | 222.92 | 500 | 220 |
19 | 0.0001 | 8.95 | 107.87 | 550 | 242 |
20 | 0.0001 | 8.62 | 116.58 | 550 | 242 |
21 | 0.0001 | 8.62 | 116.58 | 550 | 254 |
22 | 0.0161 | 5.88 | 307.45 | 550 | 254 |
23 | 0.0161 | 5.88 | 307.45 | 550 | 254 |
24 | 0.0161 | 5.88 | 307.45 | 550 | 254 |
25 | 0.00313 | 7.97 | 647.83 | 550 | 254 |
26 | 0.00708 | 9.15 | 1728.3 | 550 | 254 |
27 | 0.00313 | 7.97 | 647.85 | 150 | 10 |
28 | 0.00313 | 7.95 | 649.69 | 150 | 10 |
29 | 0.00313 | 7.97 | 647.83 | 150 | 10 |
30 | 0.00313 | 7.97 | 647.81 | 97 | 47 |
31 | 0.00298 | 6.63 | 785.96 | 190 | 60 |
32 | 0.00298 | 6.63 | 785.96 | 190 | 60 |
33 | 0.00284 | 6.66 | 794.53 | 190 | 60 |
34 | 0.00284 | 6.66 | 794.53 | 200 | 90 |
35 | 0.00277 | 7.1 | 801.32 | 200 | 90 |
36 | 0.00277 | 7.1 | 801.32 | 200 | 90 |
37 | 0.52124 | 3.33 | 1055.1 | 110 | 25 |
38 | 0.52124 | 3.33 | 1055.1 | 110 | 25 |
39 | 0.52124 | 3.33 | 1055.1 | 110 | 25 |
40 | 0.0114 | 5.35 | 148.89 | 550 | 242 |
Unit no. | |||||
1 | 0.00708 | 9.15 | 1728.3 | 114 | 36 |
2 | 0.00313 | 7.97 | 647.85 | 114 | 36 |
3 | 0.00313 | 7.95 | 649.69 | 120 | 60 |
4 | 0.00313 | 7.97 | 647.83 | 190 | 80 |
5 | 0.00313 | 7.97 | 647.81 | 97 | 47 |
6 | 0.00298 | 6.63 | 785.96 | 140 | 68 |
7 | 0.00298 | 6.63 | 785.96 | 300 | 110 |
8 | 0.00284 | 6.66 | 794.53 | 300 | 135 |
9 | 0.00284 | 6.66 | 794.53 | 300 | 135 |
10 | 0.00277 | 7.1 | 801.32 | 300 | 130 |
11 | 0.00277 | 7.1 | 801.32 | 375 | 94 |
12 | 0.52124 | 3.33 | 1055.1 | 375 | 94 |
13 | 0.52124 | 3.33 | 1055.1 | 500 | 125 |
14 | 0.52124 | 3.33 | 1055.1 | 500 | 125 |
15 | 0.0114 | 5.35 | 148.89 | 500 | 125 |
16 | 0.0016 | 6.43 | 222.92 | 500 | 125 |
17 | 0.0016 | 6.43 | 222.92 | 500 | 220 |
18 | 0.0016 | 6.43 | 222.92 | 500 | 220 |
19 | 0.0001 | 8.95 | 107.87 | 550 | 242 |
20 | 0.0001 | 8.62 | 116.58 | 550 | 242 |
21 | 0.0001 | 8.62 | 116.58 | 550 | 254 |
22 | 0.0161 | 5.88 | 307.45 | 550 | 254 |
23 | 0.0161 | 5.88 | 307.45 | 550 | 254 |
24 | 0.0161 | 5.88 | 307.45 | 550 | 254 |
25 | 0.00313 | 7.97 | 647.83 | 550 | 254 |
26 | 0.00708 | 9.15 | 1728.3 | 550 | 254 |
27 | 0.00313 | 7.97 | 647.85 | 150 | 10 |
28 | 0.00313 | 7.95 | 649.69 | 150 | 10 |
29 | 0.00313 | 7.97 | 647.83 | 150 | 10 |
30 | 0.00313 | 7.97 | 647.81 | 97 | 47 |
31 | 0.00298 | 6.63 | 785.96 | 190 | 60 |
32 | 0.00298 | 6.63 | 785.96 | 190 | 60 |
33 | 0.00284 | 6.66 | 794.53 | 190 | 60 |
34 | 0.00284 | 6.66 | 794.53 | 200 | 90 |
35 | 0.00277 | 7.1 | 801.32 | 200 | 90 |
36 | 0.00277 | 7.1 | 801.32 | 200 | 90 |
37 | 0.52124 | 3.33 | 1055.1 | 110 | 25 |
38 | 0.52124 | 3.33 | 1055.1 | 110 | 25 |
39 | 0.52124 | 3.33 | 1055.1 | 110 | 25 |
40 | 0.0114 | 5.35 | 148.89 | 550 | 242 |
Unit no. | NDE | DE | IHSWM | HPSOTVAC | SPSO | BBO |
1 | 90.59008 | 113.035 | 113.9088 | 113.9907 | 113.97 | 110.8158 |
2 | 102.8105 | 110.0641 | 110.9064 | 113.2932 | 114 | 111.0896 |
3 | 95.41785 | 96.508 | 97.402 | 120 | 109.19 | 97.40261 |
4 | 160.0214 | 180.7317 | 179.7332 | 175.0364 | 179.77 | 179.7549 |
5 | 81.69155 | 87.3028 | 88.7117 | 91 | 97 | 88.20832 |
6 | 107.7849 | 110.2516 | 139.9991 | 140 | 91.01 | 139.9886 |
7 | 278.066 | 259.0112 | 259.6372 | 260.3635 | 259.87 | 259.5935 |
8 | 268.3649 | 284.6521 | 284.6106 | 288.1256 | 286.99 | 284.6174 |
9 | 266.8508 | 286.1955 | 284.6024 | 286.9435 | 284.09 | 284.6479 |
10 | 228.3208 | 131.3615 | 130 | 130 | 204.05 | 130.0298 |
11 | 286.0884 | 243.8422 | 168.7992 | 170 | 168.4 | 94.01459 |
12 | 159.1006 | 168.7969 | 168.806 | 170 | 94 | 94.26367 |
13 | 309.307 | 302.4883 | 214.7593 | 210.0287 | 212.3 | 304.5153 |
14 | 346.2984 | 394.1255 | 394.2774 | 390.0677 | 393.76 | 394.264 |
15 | 382.4365 | 306.0514 | 304.5207 | 307.6247 | 303.62 | 304.5057 |
16 | 354.3069 | 394.3787 | 394.2762 | 300.0056 | 392.05 | 394.2472 |
17 | 442.9098 | 489.715 | 489.2787 | 487.0486 | 489.49 | 489.3273 |
18 | 440.5635 | 402.2923 | 489.2875 | 485.0793 | 489.35 | 489.3047 |
19 | 503.2849 | 516.0751 | 511.2827 | 510.541 | 512.39 | 511.3087 |
20 | 481.891 | 512.4736 | 511.2768 | 511.3472 | 511.21 | 511.2495 |
21 | 506.4032 | 524.042 | 523.2884 | 524.9522 | 522.61 | 523.3217 |
22 | 496.2304 | 524.0563 | 523.2794 | 526 | 523.65 | 523.3144 |
23 | 512.3193 | 524.3457 | 523.2772 | 523.9211 | 523.06 | 523.3629 |
24 | 511.7555 | 524.2132 | 523.2928 | 525.612 | 520.72 | 523.2883 |
25 | 507.4037 | 525.7952 | 523.3047 | 521.02 | 524.86 | 523.2989 |
26 | 493.267 | 522.6361 | 523.2872 | 520.1457 | 525.22 | 523.2802 |
27 | 10.30096 | 10.1786 | 10 | 10 | 10 | 10.02817 |
28 | 19.53625 | 12.3112 | 10.0022 | 10 | 10 | 10.00321 |
29 | 10.68301 | 10.8716 | 10.0018 | 10 | 10 | 10.0288 |
30 | 87.11869 | 90.3572 | 88.362 | 89.7002 | 87.64 | 88.14595 |
31 | 184.1883 | 187.5783 | 190 | 190 | 190 | 189.9913 |
32 | 174.4745 | 167.4291 | 190 | 190 | 190 | 189.9888 |
33 | 169.4674 | 177.4801 | 189.9935 | 190 | 190 | 189.9998 |
34 | 171.0925 | 166.2373 | 164.7992 | 167.0209 | 200 | 164.8452 |
35 | 176.4929 | 185.5927 | 164.8923 | 200 | 167.18 | 192.9876 |
36 | 172.1041 | 173.4381 | 164.864 | 200 | 172.12 | 199.9876 |
37 | 94.5799 | 89.3767 | 110 | 110 | 110 | 109.9941 |
38 | 85.26457 | 91.0112 | 110 | 110 | 110 | 109.9992 |
39 | 93.85836 | 91.6815 | 109.9965 | 110 | 95.58 | 109.9833 |
40 | 493.3801 | 512.0169 | 511.2828 | 511.1323 | 510.85 | 511.2794 |
0 | 0.001 | 0 | 0 | 0 | 0.28 | |
Total Output Power | 10, 500.00 | 10, 500.00 | 10, 500.00 | 10, 500.00 | 10, 500.00 | 10, 500.28 |
Min Cost ($/Hr) | 1, 21, 721.62 | 1, 21, 974.50 | 1, 21, 416.26 | 1, 21, 070.64 | 1, 22, 049.66 | 1, 21, 479.50 |
Mean Cost ($/Hr) | 1, 21, 992.20 | 1, 22, 580.30 | 1, 21, 553.42 | 1, 21, 075.74 | 1, 22, 327.36 | 1, 21, 512.05 |
Unit no. | NDE | DE | IHSWM | HPSOTVAC | SPSO | BBO |
1 | 90.59008 | 113.035 | 113.9088 | 113.9907 | 113.97 | 110.8158 |
2 | 102.8105 | 110.0641 | 110.9064 | 113.2932 | 114 | 111.0896 |
3 | 95.41785 | 96.508 | 97.402 | 120 | 109.19 | 97.40261 |
4 | 160.0214 | 180.7317 | 179.7332 | 175.0364 | 179.77 | 179.7549 |
5 | 81.69155 | 87.3028 | 88.7117 | 91 | 97 | 88.20832 |
6 | 107.7849 | 110.2516 | 139.9991 | 140 | 91.01 | 139.9886 |
7 | 278.066 | 259.0112 | 259.6372 | 260.3635 | 259.87 | 259.5935 |
8 | 268.3649 | 284.6521 | 284.6106 | 288.1256 | 286.99 | 284.6174 |
9 | 266.8508 | 286.1955 | 284.6024 | 286.9435 | 284.09 | 284.6479 |
10 | 228.3208 | 131.3615 | 130 | 130 | 204.05 | 130.0298 |
11 | 286.0884 | 243.8422 | 168.7992 | 170 | 168.4 | 94.01459 |
12 | 159.1006 | 168.7969 | 168.806 | 170 | 94 | 94.26367 |
13 | 309.307 | 302.4883 | 214.7593 | 210.0287 | 212.3 | 304.5153 |
14 | 346.2984 | 394.1255 | 394.2774 | 390.0677 | 393.76 | 394.264 |
15 | 382.4365 | 306.0514 | 304.5207 | 307.6247 | 303.62 | 304.5057 |
16 | 354.3069 | 394.3787 | 394.2762 | 300.0056 | 392.05 | 394.2472 |
17 | 442.9098 | 489.715 | 489.2787 | 487.0486 | 489.49 | 489.3273 |
18 | 440.5635 | 402.2923 | 489.2875 | 485.0793 | 489.35 | 489.3047 |
19 | 503.2849 | 516.0751 | 511.2827 | 510.541 | 512.39 | 511.3087 |
20 | 481.891 | 512.4736 | 511.2768 | 511.3472 | 511.21 | 511.2495 |
21 | 506.4032 | 524.042 | 523.2884 | 524.9522 | 522.61 | 523.3217 |
22 | 496.2304 | 524.0563 | 523.2794 | 526 | 523.65 | 523.3144 |
23 | 512.3193 | 524.3457 | 523.2772 | 523.9211 | 523.06 | 523.3629 |
24 | 511.7555 | 524.2132 | 523.2928 | 525.612 | 520.72 | 523.2883 |
25 | 507.4037 | 525.7952 | 523.3047 | 521.02 | 524.86 | 523.2989 |
26 | 493.267 | 522.6361 | 523.2872 | 520.1457 | 525.22 | 523.2802 |
27 | 10.30096 | 10.1786 | 10 | 10 | 10 | 10.02817 |
28 | 19.53625 | 12.3112 | 10.0022 | 10 | 10 | 10.00321 |
29 | 10.68301 | 10.8716 | 10.0018 | 10 | 10 | 10.0288 |
30 | 87.11869 | 90.3572 | 88.362 | 89.7002 | 87.64 | 88.14595 |
31 | 184.1883 | 187.5783 | 190 | 190 | 190 | 189.9913 |
32 | 174.4745 | 167.4291 | 190 | 190 | 190 | 189.9888 |
33 | 169.4674 | 177.4801 | 189.9935 | 190 | 190 | 189.9998 |
34 | 171.0925 | 166.2373 | 164.7992 | 167.0209 | 200 | 164.8452 |
35 | 176.4929 | 185.5927 | 164.8923 | 200 | 167.18 | 192.9876 |
36 | 172.1041 | 173.4381 | 164.864 | 200 | 172.12 | 199.9876 |
37 | 94.5799 | 89.3767 | 110 | 110 | 110 | 109.9941 |
38 | 85.26457 | 91.0112 | 110 | 110 | 110 | 109.9992 |
39 | 93.85836 | 91.6815 | 109.9965 | 110 | 95.58 | 109.9833 |
40 | 493.3801 | 512.0169 | 511.2828 | 511.1323 | 510.85 | 511.2794 |
0 | 0.001 | 0 | 0 | 0 | 0.28 | |
Total Output Power | 10, 500.00 | 10, 500.00 | 10, 500.00 | 10, 500.00 | 10, 500.00 | 10, 500.28 |
Min Cost ($/Hr) | 1, 21, 721.62 | 1, 21, 974.50 | 1, 21, 416.26 | 1, 21, 070.64 | 1, 22, 049.66 | 1, 21, 479.50 |
Mean Cost ($/Hr) | 1, 21, 992.20 | 1, 22, 580.30 | 1, 21, 553.42 | 1, 21, 075.74 | 1, 22, 327.36 | 1, 21, 512.05 |
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