An improved adaptive differential evolution optimizer for non-convex Economic Dispatch Problems

Abstract The present paper proposes an improved adaptive differential evolution algorithm, the IL-SHADE algorithm, to solve Economic Dispatch Problems (EDPs) taking into account practical constraints, such as transmission network losses, ramp rate limit, prohibited operation zone and valve point effect. The IL-SHADE algorithm is introduced as an improved version of the L-SHADE algorithm (Success-History based Adaptive Differential Evolution algorithm with Linear population size reduction). The proposed algorithm is first tested on eight CEC’05 standard benchmark test functions. Then, the efficiency of the proposed optimizer is demonstrated by solving different practical EDPs related to three IEEE power test systems, the IEEE 6-unit, 40-unit and 140-unit test systems. The comparison with various recent state-of-the-art approaches proves that IL-SHADE outperforms the L-SHADE and other cited approaches. Finally, the Wilcoxon sign rank test is used to validate the results.

[1]  Mohammad Rasoul Narimani,et al.  A multi-objective framework for multi-area economic emission dispatch , 2018, Energy.

[2]  E. S. Ali,et al.  Combined economic and emission dispatch solution using Flower Pollination Algorithm , 2016 .

[3]  P. K. Chattopadhyay,et al.  Biogeography-Based Optimization for Different Economic Load Dispatch Problems , 2010, IEEE Transactions on Power Systems.

[4]  L. Coelho,et al.  Solving non-smooth economic dispatch by a new combination of continuous GRASP algorithm and differential evolution , 2017 .

[5]  D. Karaboga,et al.  A Simple and Global Optimization Algorithm for Engineering Problems: Differential Evolution Algorithm , 2004 .

[6]  Shilpa Suresh,et al.  Modified differential evolution algorithm for contrast and brightness enhancement of satellite images , 2017, Appl. Soft Comput..

[7]  Arthur C. Sanderson,et al.  JADE: Adaptive Differential Evolution With Optional External Archive , 2009, IEEE Transactions on Evolutionary Computation.

[8]  C. Jiang,et al.  An adaptive differential evolution algorithm with an aging leader and challengers mechanism , 2017, Appl. Soft Comput..

[9]  Hussain Shareef,et al.  Lightning search algorithm , 2015, Appl. Soft Comput..

[10]  Abhishek Rajan,et al.  Optimum economic and emission dispatch using exchange market algorithm , 2016 .

[11]  Allen J. Wood,et al.  Power Generation, Operation, and Control , 1984 .

[12]  Li Li,et al.  A differential evolution particle swarm optimizer for various types of multi-area economic dispatch problems , 2016 .

[13]  Tharam S. Dillon,et al.  Experimental study of a new hybrid PSO with mutation for economic dispatch with non-smooth cost function , 2010 .

[14]  Jong-Bae Park,et al.  An Improved Particle Swarm Optimization for Nonconvex Economic Dispatch Problems , 2010 .

[15]  Mohammad Rasoul Narimani,et al.  A comprehensive study of practical economic dispatch problems by a new hybrid evolutionary algorithm , 2017, Appl. Soft Comput..

[16]  Pascal Bouvry,et al.  Particle swarm optimization: Hybridization perspectives and experimental illustrations , 2011, Appl. Math. Comput..

[17]  Umamaheswari krishnasamy,et al.  Hybrid weighted probabilistic neural network and biogeography based optimization for dynamic economic dispatch of integrated multiple-fuel and wind power plants , 2016 .

[18]  Alex S. Fukunaga,et al.  Success-history based parameter adaptation for Differential Evolution , 2013, 2013 IEEE Congress on Evolutionary Computation.

[19]  W. T. Elsayed,et al.  A Fully Decentralized Approach for Solving the Economic Dispatch Problem , 2015, IEEE Transactions on Power Systems.

[20]  I. Ngamroo,et al.  Multiple tabu search algorithm for economic dispatch problem considering valve-point effects , 2011 .

[21]  Rawaa Dawoud Al-Dabbagh,et al.  Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy , 2018, Swarm Evol. Comput..

[22]  Alex S. Fukunaga,et al.  Improving the search performance of SHADE using linear population size reduction , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[23]  Shichang Cui,et al.  Data for: Distributed Auction Optimization Algorithm for the Nonconvex Economic Dispatch Problem Based on the Gossip Communication Mechanism , 2018 .

[24]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[25]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[26]  Pandian Vasant,et al.  A holistic review on optimization strategies for combined economic emission dispatch problem , 2018 .

[27]  Zwe-Lee Gaing,et al.  Particle swarm optimization to solving the economic dispatch considering the generator constraints , 2003 .

[28]  Provas Kumar Roy,et al.  Grey wolf optimization applied to economic load dispatch problems , 2016 .

[29]  Anastasios G. Bakirtzis,et al.  Genetic algorithm solution to the economic dispatch problem , 1994 .

[30]  Malabika Basu,et al.  Kinetic gas molecule optimization for nonconvex economic dispatch problem , 2016 .

[31]  Adriane Beatriz de Souza Serapião,et al.  Cuckoo Search for Solving Economic Dispatch Load Problem , 2013 .

[32]  D. Devaraj,et al.  An Improved Differential Evolution algorithm for congestion management in the presence of wind turbine generators , 2018 .

[33]  Shang-Ho Tsai,et al.  Economic dispatch of chiller plant by improved ripple bee swarm optimization algorithm for saving energy , 2016 .

[34]  P. N. Suganthan,et al.  Differential Evolution: A Survey of the State-of-the-Art , 2011, IEEE Transactions on Evolutionary Computation.

[35]  Janez Brest,et al.  iL-SHADE: Improved L-SHADE algorithm for single objective real-parameter optimization , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[36]  Ponnuthurai N. Suganthan,et al.  Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood for solving CEC2017 benchmark problems , 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).

[37]  T. Jayabarathi,et al.  Economic dispatch using hybrid grey wolf optimizer , 2016 .

[38]  Abdennaceur Kachouri,et al.  Coordinated consensus for smart grid economic environmental power dispatch with dynamic communication network , 2018 .

[39]  X. X. Zhou,et al.  Fast $\lambda$ -Iteration Method for Economic Dispatch With Prohibited Operating Zones , 2014, IEEE Transactions on Power Systems.

[40]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[41]  Hongbin Sun,et al.  A Bi-Level Branch and Bound Method for Economic Dispatch With Disjoint Prohibited Zones Considering Network Losses , 2015, IEEE Transactions on Power Systems.

[42]  Abbas Khosravi,et al.  An intelligent θ-Modified Bat Algorithm to solve the non-convex economic dispatch problem considering practical constraints , 2016 .

[43]  Pinar Çivicioglu,et al.  Backtracking Search Optimization Algorithm for numerical optimization problems , 2013, Appl. Math. Comput..

[44]  A. Kai Qin,et al.  Self-adaptive differential evolution algorithm for numerical optimization , 2005, 2005 IEEE Congress on Evolutionary Computation.

[45]  Bijay Ketan Panigrahi,et al.  Simulated annealing approach for solving economic load dispatch problems with valve point loading effects , 2013 .

[46]  Provas Kumar Roy,et al.  Opposition-based krill herd algorithm applied to economic load dispatch problem , 2018, Ain Shams Engineering Journal.

[47]  Mohammad Rasoul Narimani,et al.  Multi-objective dynamic distribution feeder reconfiguration in automated distribution systems , 2018 .

[48]  Yu Liu,et al.  A novel bat algorithm with habitat selection and Doppler effect in echoes for optimization , 2015, Expert Syst. Appl..

[49]  M. Pandit,et al.  Self-Organizing Hierarchical Particle Swarm Optimization for Nonconvex Economic Dispatch , 2008, IEEE Transactions on Power Systems.