Optimal Economic Dispatch of Fuel Cost Based on Intelligent Monkey King Evolutionary Algorithm
Monkey King evolutionary algorithm (MKEA) is a new type and innovation of gene method that can be more effective evolution of the algorithm to reach goal or objective function. In this study and research is applied the monkey king evolutionary algorithm is used to apply the evolutionary particle to find the optimal power flow of system and calculate the complex power of each line, bus and to minimize power generation cost of the power plant. In order to study the practicability of the algorithm, it is applied to the standard IEEE 5bus load flow test system, and its convergence characteristic curve is observed and compared with the genetic algorithm (GA). The experimental results show that the MKEA can effectively solve the power system optimal power flow problem and this method is find the global solution not local solution that be confirmed in minimum fuel cost of generator of power plants. The minimum fuel cost obtained by MKE and GA is 5369.55 and 5422.0 US Dollars, respectively, when the number of population particles is 100 and the number of iterations is 300 that compared with GA which is 7.6% lower than GA. The results show that MKE has the obvious superiority to find the global solution.
KeywordsMonkey king evolutionary algorithm Optimal power flow Minimum fuel cost IEEE 5bus Convergence characteristic
- 7.Brest, J., Zamuda, A., Bošković, B.: Adaptation in the Differential Evolution, vol. 18, pp. 53–68. Springer, Heidelberg (2015)Google Scholar
- 8.Liang, J.J., Zhang, W.X., Qu, B.Y., Chen, T.J.: Multiobjective Dynamic Multi-Swarm Particle Swarm Optimization for Environmental/Economic Dispatch Problem, vol. 7389, pp. 657–664. Springer, Heidelberg (2012)Google Scholar
- 10.Ling, W.X., Wang, Y.X.: Using Modular Neural Network with Artificial Bee Colony Algorithm for Classification, vol. 7928, pp. 396–403. Springer, Heidelberg (2013)Google Scholar
- 12.Chinnasri, W.: Adaptive probability of crossover and mutation in genetic algorithm on university course timetabling problem. In: 2013 IEEE International Conference on Computer Science and Automation Engineering, vol. 24(4), pp. 656–667 (2002)Google Scholar
- 17.Meng, Z., Pan, J.S.: A Simple and Accurate Global Optimizer for Continuous Spaces Optimization, Genetic and Evolutionary Computing, pp. 121–129. Springer, Heidelberg (2015)Google Scholar