Milena JEVTIC, Nenad JOVANOVIC, Jordan RADOSAVLJEVIC

Solving a combined economic emission dispatch problem using adaptive wind driven optimization

In this paper, the adaptive wind driven optimization (AWDO) algorithm is applied for solving the combinedeconomic emission dispatch (CEED) problem. AWDO is one of the newest hybrid algorithms, which optimizes theselection of coefficients at each iteration, eliminating the need for tuning the coefficients. The evaluation of AWDOperformances is carried out on the standard IEEE 30-bus test system with 6 generating units and with various cost curvenatures. The results of AWDO use with the test system are compared against the results of use of 3 algorithms: the mothswarm algorithm, firefly algorithm, and hybrid particle swarm optimization and gravitational search algorithm, whichwere proposed in recent literature for solving this problem. The present paper shows that AWDO gives an accurate andeffective solution of the CEED problem and outperforms the other tested algorithms.

PDF

___

[1] Jevtić M, Jovanović N, Radosavljivić J, Klimenta D. Moth swarm algorithm for solving combined economic and emission dispatch problem. Elektron Elektrotech 2017; 23: 21-28.
2] Chen G, Ding X. Optimal economic dispatch with valve loading effect using self-adaptive firefly algorithm. Appl Intell 2015; 42: 276-288.
[3] Zhan ZH, Zhang J, Li Y, Chung HSH. Adaptive particle swarm optimization. IEEE T Syst Man Cy B 2009; 39: 1362-1381.
[4] Vlachogiannis JG, Lee KY. Economic load dispatch - a comparative study on heuristic optimization techniques with an improved coordinated aggregation-based PSO. IEEE T Power Syst 2009; 24: 991-1001.
[5] Bayraktar Z, Komurcu M. Adaptive wind driven optimization. In: 9th EAI International Conference on Bio-inspired Information and Communications Technologies; 3–5 December 2015; New York City, NY, USA. pp. 124-127.
[6] Benasla L, Belmadani A, Rahli M. Spiral optimization algorithm for solving combined economic and emission dispatch. Int J Elec Power 2014; 62: 163-174.
[7] Bhattacharya A, Chattopadhyay PK. Solving economic emission load dispatch problems using hybrid differential evolution. Appl Soft Comput 2011; 11: 2526-2537.
[8] Güvenç U, Sönmez Y, Duman S, Yörükeren N. Combined economic and emission dispatch solution using gravitational search algorithm. Sci Iran 2012; 19: 1754-1762.
[9] Aydin D, Ozyon S, Yasar C, Liao T. Artificial bee colony algorithm with dynamic population size to combined economic and emission dispatch problem. Int J Elec Power 2014; 54: 144-153.
[10] Bayraktar Z, Komurcu M, Bossard JA, Werner DH. The wind driven optimization technique and its application in electromagnetics. IEEE T Antenn Propag 2013; 61: 2745-2757.
[11] Gregory MD, Bayraktar Z, Werner DH. Fast optimization of electromagnetic design problems using the covariance matrix adaptation evolutionary strategy. IEEE T Antenn Propag 2011; 59: 1275-1285.
[12] Mohamed AA, Mohamed YS, El-Gaafary AAM, Hemeida AM. Optimal power flow using moth swarm algorithm. Electr Pow Syst Res 2017; 142: 190-206.
[13] Yang XS. Firefly algorithm, Levy flights and global optimization. In: Bramer M, Ellis R, Petridis M, editors. Research and Development in Intelligent Systems XXVI. London, UK: Springer London, 2010. pp. 209-218.
[14] Mirjalli S, Hashim SZM, Sardroudi HM. Training feedforward neural networks using hybrid particle swarm and gravitational search algorithm. App Math Comput 2012; 218: 11125-11137.
[15] Radosavljević J. A solution to the combined economic and emission dispatch using hybrid PSOGSA algorithm. Appl Artif Intell 2016; 30: 445-474.
[16] Hota PK, Barisal AK, Chakrabarti R. Economic emission load dispatch through fuzzy based bacterial foraging algorithm. Int J Elec Power 2010; 32: 794-803.
[17] Abido MA. Multiobjective particle swarm optimization for environmental/economic dispatch problem. Electr Pow Syst Res 2009; 79: 1105-1113.
[18] Hazra J, Sinha AK. A multi-objective power flow using particle swarm optimization. Eur T Electr Power 2011; 21: 1028-1045.
[19] Abou El Ela AA, Abido MA, Spea SR. Differential evolution algorithm for emission constrained economic power dispatch problem. Electr Pow Syst Res 2010; 80: 1286-1292.
[20] Gong DW, Zhang Y, Qi CL. Environmental/economic power dispatch using a hybrid multi-objective optimization algorithm. Int J Elec Power 2010; 32: 607-614.
[21] Garcia S, Molina D, Lozano M, Herrera F. A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization. J Heuristics 2009; 15: 617-644.