Optimization of the Economic Dispatch Problem by Considering the Emission Dispatch with Using the AMPSO

Optimization of the Economic Dispatch Problem by Considering the Emission Dispatch with Using the AMPSO

Mehdi Neyestani* Maliheh Maghfoori Farsangi

Department of Electrical Engineering, Esfarayen University of Technology, Esfarayen, North Khorasan 98195, Iran

Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman 98195, Iran

Corresponding Author Email: 
M.Neyestani@esfarayen.ac.ir
Page: 
60-71
|
DOI: 
https://doi.org/10.18280/ama_c.730205
Received: 
20 March 2018
| |
Accepted: 
16 May 2018
| | Citation

OPEN ACCESS

Abstract: 

In this paper, the problem of economic dispatch (ED), which is a problem with a nonlinear cost function, is solved using the proposed method. The ED problem, in addition to having a nonlinear function, also has a series of equal and unequal constraints that must be respected. Another important consideration when generating energy by power plants is, in addition to the cost, the issue of polluting the environment. In other words, in order to provide the power needed by consumers, in this issue two goals, reducing production costs and reducing the amount of emission, can be raised. This article covers all of the above issues. In this paper, an ultra-innovative algorithm is proposed to solve the problem. The proposed algorithm is based on the Particle Swarm Optimization algorithm. The proposed method is implemented on the systems under study with different conditions and the results obtained by AMPSO method are evaluated by other methods such as SA, NSGA-II and NSGA-III. The results show that the AMPSO produces optimal or nearly optimal solutions for the study systems.

Keywords: 

economic dispatch, emission dispatch, multi-objective, Particle Swarm optimization

1. Introduction
2. Formulation of the Problem
3. PSO, MPSO and AMPSO Algorithms
4. Systems Studied and Simulated
5. Conclusion
  References

[1] Trefny FJ, Lee KY. (1981). Economic fuel dispatch. IEEE Transactions on Power Apparatus and Systems (7): 3468-3477. https://doi.org/10.1109/TPAS.1981.316690 

[2] He D, Dong G, Wang F, Mao Z. (2011). Optimization of dynamic economic dispatch with valve-point effect using chaotic sequence based differential evolution algorithms. Energy Conversion and Management 52(2): 1026-1032. https://doi.org/10.1016/j.enconman.2010.08.031

[3] Le KD, Golden JL, Stansberry CJ, Vice RL, Wood JT, Ballance J, Ookubo M. (1995). Potential impacts of clean air regulations on system operations. IEEE Transactions on Power Systems 10(2): 647-656. https://doi.org/10.1109/59.387899 

[4] Talaq JH, El-Hawary F, El-Hawary ME. (1994). A summary of environmental/economic dispatch algorithms. IEEE Transactions on Power Systems 9(3): 1508-1516. https://doi.org/10.1109/59.336110 

[5] Elaiw AM, Xia X, Shehata AM. (2013). Hybrid DE-SQP and hybrid PSO-SQP methods for solving dynamic economic emission dispatch problem with valve-point effects. Electric Power Systems Research 103: 192-200. https://doi.org/10.1016/j.epsr.2013.05.015

[6] Yang J, Li F, Freeman L. (2003). A market simulation program for the standard market design and generation/transmission planning. In Power Engineering Society General Meeting, IEEE 1: 442-446. https://doi.org/10.1109/PES.2003.1267217 

[7] Chao H, Li F, Trinh LH, Pan J, Gopinathan M, Pillo DJ. (2004). Market based transmission planning considering reliability and economic performances. In Probabilistic Methods Applied to Power Systems, 2004 International Conference on IEEE, pp. 557-562.

[8] Hetzer J, David CY, Bhattarai K. (2008). An economic dispatch model incorporating wind power. IEEE Transactions on energy conversion 23(2): 603-611. https://doi.org/10.1109/TEC.2007.914171 

[9] Miranda V, Hang PS. (2005). Economic dispatch model with fuzzy wind constraints and attitudes of dispatchers. IEEE Transactions on Power Systems 20(4): 2143-2145. https://doi.org/10.1109/TPWRS.2005.857930 

[10] Wang CR, Yuan HJ, Huang ZQ, Zhang JW, Sun CJ. (2005). A modified particle swarm optimization algorithm and its application in optimal power flow problem. In Machine Learning and Cybernetics, Proceedings of 2005 International Conference on IEEE 5: 2885-2889. https://doi.org/10.1109/ICMLC.2005.1527435 

[11] Vlachogiannis JG, Lee KY. (2009). Economic load dispatch—A comparative study on heuristic optimization techniques with an improved coordinated aggregation-based PSO. IEEE Transactions on Power Systems 24(2): 991-1001. https://doi.org/10.1109/TPWRS.2009.2016524 

[12] AlRashidi MR, AlHajri MF, El-Hawary ME. (2010). Enhanced particle swarm optimization approach for solving the non-convex optimal power flow. World Academy of Science, Engineering and Technology 62(2): 651-655.

[13] Walters DC, Sheble GB. (1993). Genetic algorithm solution of economic dispatch with valve point loading. IEEE Transactions on Power Systems 8(3): 1325-1332. https://doi.org/10.1109/59.260861 

[14] Neyestani M, Farsangi MM, Nezamabadi-Pour H, Lee KY. (2009). A modified particle swarm optimization for economic dispatch with nonsmooth cost functions. IFAC Proceedings Volumes 42(9): 267-272. https://doi.org/10.3182/20090705-4-SF-2005.00048

[15] Gaing ZL, Chang RF. (2006). Security-Constrained economic scheduling of generation considering generator constraints. In Power System Technology, 2006. PowerCon 2006. International Conference on IEEE, 1-7. https://doi.org/10.1109/ICPST.2006.321675 

[16] Sharma A, Goyal GR. (2017). Solution of an ELD problem with valve-point effect using artificial intelligence techniques. Mathematical Modelling of Engineering Problems 4(3): 132-37. https://doi.org/10.18280/mmep.040304 

[17] Ratniyomchai T, Oonsivilai A, Pao-La-Or P, Kulworawanichpong T. (2010). Particle swarm optimization for solving combined economic and emission dispatch problems. In 5th Proceedings IASME/WSEAS International Conference on Energy & Environment, 211-216.

[18] Cetinkaya N. (2009). Optimization algorithm for combined economic and emission dispatch with security constraints. In Int. Conf. Comp. Sci. Appl. ICCSA, 150-153.

[19] Uğur GV. (2010). Combined economic emission dispatch solution using genetic algorithm based on similarity crossover. Scientific Research and Essays 5(17): 2451-2456.

[20] Hemamalini S, Simon SP. (2009). Economic/emission load dispatch using artificial bee colony algorithm. iPi, 1, 2.

[21] Sonmez Y. (2011). Multi-objective environmental /economic dispatch solution with penalty factor using Artificial Bee Colony algorithm. Scientific Research and Essays 6(13): 2824-2831. https://doi.org/10.5897/SRE11.408

[22] Palanichamy C, Babu NS. (2008). Analytical solution for combined economic and emissions dispatch. Electric Power Systems Research 78(7): 1129-1137. https://doi.org/10.1016/j.epsr.2007.09.005

[23] Balamurugan R, Subramanian S. (2007). A simplified recursive approach to combined economic emission dispatch. Electric Power Components and Systems 36(1): 17-27. https://doi.org/10.1080/15325000701473742

[24] Chen YM, Wang WS. (2010). A particle swarm approach to solve environmental/economic dispatch problem. International Journal of Industrial Engineering Computations 1(2): 157-172. https://doi.org/10.5267/j.ijiec.2010.02.006

[25] Sen GD, Sharma J, Goyal GR, Singh AK. (2017). A multi-objective PSO (MOPSO) algorithm for optimal active power dispatch with pollution control. Mathematical Modelling of Engineering Problems 4(3): 113-119. https://doi.org/10.18280/mmep.040301

[26] Gupta A, Swarnkar KK, Wadhwani AK. (2012). Combined economic emission dispatch problem using particle swarm optimization. International Journal of Computer Applications 49(6). https://doi.org/10.5120/7628-0695

[27] Roy PK, Ghoshal SP, Thakur SS. (2010). Combined economic and emission dispatch problems using biogeography-based optimization. Electrical Engineering 92(4-5): 173-184. https://doi.org/10.1007/s00202-010-0173-3

[28] Wood AJ, Wollenberg BF. (2012). Power generation, operation, and control. John Wiley & Sons. (3rd ed.). Retrieved from http://as.wiley.com/WileyCDA/WileyTitle/productCd-0471790559.html

[29] Jayakumar DN, Venkatesh P. (2014). Glowworm swarm optimization algorithm with topsis for solving multiple objective environmental economic dispatch problem. Applied Soft Computing 23: 375-386. https://doi.org/10.1016/j.asoc.2014.06.049

[30] Kennedy R. (1995). J. and Eberhart, Particle swarm optimization. In Proceedings of IEEE International Conference on Neural Networks IV 1000: 1942–1948.

[31] Neyestani M, Farsangi MM, Nezamabadi-Pour H. (2010). A modified particle swarm optimization for economic dispatch with non-smooth cost functions. Engineering Applications of Artificial Intelligence 23(7): 1121-1126. https://doi.org/10.1016/j.engappai.2010.06.006

[32] Yandri HJ. (2016). Combined economic emission dispatch solution using simulated annealing algorithm. IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE) 11(5): 141-148.

[33] Rughooputh HC, King RA. (2003). Environmental/economic dispatch of thermal units using an elitist multiobjective evolutionary algorithm. In Industrial Technology. 2003 IEEE International Conference on IEEE 1: 48-53. https://doi.org/10.1109/ICIT.2003.1290230 

[34] Basu M. (2011). Economic environmental dispatch using multi-objective differential evolution. Applied soft computing 11(2): 2845-2853. https://doi.org/10.1016/j.asoc.2010.11.014

[35] Bhesdadiya RH, Trivedi IN, Jangir P, Jangir N, Kumar A. (2016). An NSGA-III algorithm for solving multi-objective economic/environmental dispatch problem. Cogent Engineering 3(1): 1269383. https://doi.org/10.1080/23311916.2016.1269383