Multi-objective Modified Grey Wolf Optimizer for Optimal Power Flow

This paper presents a new Single/Multi-Objective Optimization algorithm inspired by hunting behavior of Grey Wolves (S/MOGWO). Modification is introduced to enhance the convergence rate of GWO. The proposed algorithm has been applied to minimize the emission grades of deleterious pollutants and to reduce the generating cost individually. Also, the proposed MOGWO is used to generate Pareto-optimal solutions for simultaneous minimization of the environmental pollution emissions along with the economic cost. Furthermore, fuzzy decision approach process is implemented to rank and extract the global Pareto-optimal solutions as the most suitable non-dominated solution. The effectiveness of the proposed methodology is tested on IEEE 30-bus system. The generation and security constraints are incorporated into the objective function to achieve a valid and accurate solution. The comparative study with different other techniques confirms the primacy of the proposed algorithm and its potential to solve the OPF problem in single and multi-objective optimization space.

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

[2]  Samir Sayah,et al.  Modified differential evolution algorithm for optimal power flow with non-smooth cost functions , 2008 .

[3]  Mousumi Basu,et al.  Group Search Optimization for Solution of Different Optimal Power Flow Problems , 2016 .

[4]  Jianlin Hu,et al.  Impacts of power generation on air quality in China—part I: An overview , 2017 .

[5]  Mingbo Liu,et al.  Multiobjective Stochastic Economic Dispatch With Variable Wind Generation Using Scenario-Based Decomposition and Asynchronous Block Iteration , 2016, IEEE Transactions on Sustainable Energy.

[6]  Xuyan Tu,et al.  Algorithm of Marriage in Honey Bees Optimization Based on the Wolf Pack Search , 2007, The 2007 International Conference on Intelligent Pervasive Computing (IPC 2007).

[7]  Joydeep Mitra,et al.  An emission-constrained approach to power system expansion planning , 2016 .

[8]  Weerakorn Ongsakul,et al.  Optimal Power Flow by Improved Evolutionary Programming , 2006 .

[9]  Wanxing Sheng,et al.  Research and practice on typical modes and optimal allocation method for PV-Wind-ES in Microgrid , 2015 .

[10]  Tarek Bouktir,et al.  Economic power dispatch of power systems with pollution control using artificial bee colony optimization , 2013 .

[11]  Taher Niknam,et al.  A modified shuffle frog leaping algorithm for multi-objective optimal power flow , 2011 .

[12]  Claudio A. Roa-Sepulveda,et al.  A solution to the optimal power flow using simulated annealing , 2003 .

[13]  Xin Yao,et al.  A New Dominance Relation-Based Evolutionary Algorithm for Many-Objective Optimization , 2016, IEEE Transactions on Evolutionary Computation.

[14]  O. Alsac,et al.  Optimal Load Flow with Steady-State Security , 1974 .

[15]  Behnam Mohammadi-Ivatloo,et al.  Probabilistic multi-objective optimal power flow considering correlated wind power and load uncertainties , 2016 .

[16]  B. Allaoua,et al.  Optimal Power Flow Solution Using Ant Manners for Electrical Network , 2009 .

[17]  Long Wang,et al.  The Crucial Problem of the NSS in the Ecommerce , 2007 .

[18]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[19]  Abderrahim Belmadani,et al.  Spiral Optimization Algorithm for solving Combined Economic and Emission Dispatch , 2014 .

[20]  Jing J. Liang,et al.  Economic emission dispatch problems with stochastic wind power using summation based multi-objective evolutionary algorithm , 2016, Inf. Sci..

[21]  H. R. E. H. Bouchekara,et al.  Optimal power flow with emission and non-smooth cost functions using backtracking search optimization algorithm , 2016 .

[22]  Weerakorn Ongsakul,et al.  Performance enhancement of online energy scheduling in a radial utility distribution microgrid , 2016 .

[23]  Q. H. Wu,et al.  Optimal Power System Dispatch With Wind Power Integrated Using Nonlinear Interval Optimization and Evidential Reasoning Approach , 2016, IEEE Transactions on Power Systems.

[24]  A. A. El-Keib,et al.  Economic dispatch in view of the Clean Air Act of 1990 , 1994 .

[25]  Ali Ghasemi,et al.  Modeling of Wind/Environment/Economic Dispatch in power system and solving via an online learning meta-heuristic method , 2016, Appl. Soft Comput..

[26]  Reza Effatnejad,et al.  Comprehensive Learning Particle Swarm Optimization (CLPSO) for Multi-objective Optimal Power Flow , 2013 .

[27]  R. Coppinger,et al.  Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations , 2011, Behavioural Processes.

[28]  Salkuti Surender Reddy,et al.  Optimal Power Flow using Glowworm Swarm Optimization , 2016 .

[29]  C.A. Roa-Sepulveda,et al.  A solution to the optimal power flow using simulated annealing , 2001, 2001 IEEE Porto Power Tech Proceedings (Cat. No.01EX502).

[30]  Kit Po Wong,et al.  Evolutionary programming based optimal power flow algorithm , 1999 .

[31]  Pierluigi Siano,et al.  Multiobjective Optimal Design of Photovoltaic Synchronous Boost Converters Assessing Efficiency, Reliability, and Cost Savings , 2015, IEEE Transactions on Industrial Informatics.

[32]  Yalin Chen,et al.  A modified MOEA/D approach to the solution of multi-objective optimal power flow problem , 2016, Appl. Soft Comput..

[33]  Ertuğrul Çam,et al.  A new hybrid algorithm with genetic-teaching learning optimization (G-TLBO) technique for optimizing of power flow in wind-thermal power systems , 2016 .

[34]  Nikolaos G. Paterakis,et al.  Multi-objective reconfiguration of radial distribution systems using reliability indices , 2016, 2016 IEEE/PES Transmission and Distribution Conference and Exposition (T&D).

[35]  Wu Husheng,et al.  A uncultivated wolf pack algorithm for high-dimensional functions and its application in parameters optimization of PID controller , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[36]  Bo Xing,et al.  Emerging Biology-based CI Algorithms , 2014 .

[37]  Wu,et al.  The Wolf Colony Algorithm and Its Application , 2011 .

[38]  Ruben Romero,et al.  A Multi-Objective Distribution System Expansion Planning Incorporating Customer Choices on Reliability , 2016, IEEE Transactions on Power Systems.

[39]  Xiaodong Li,et al.  DMMOGSA: Diversity-enhanced and memory-based multi-objective gravitational search algorithm , 2016, Inf. Sci..

[40]  Bo Xing,et al.  Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms , 2013 .

[41]  K. Fahd,et al.  Optimal Power Flow Using Tabu Search Algorithm , 2002 .

[42]  Shu-Cherng Fang,et al.  Non-L-R Type Fuzzy Parameters in Mathematical Programming Problems , 2014, IEEE Transactions on Fuzzy Systems.