Newly-Developed Swarm Intelligence Algorithms Applied to Renewable Energy-Based Load Dispatch Real-World Problems

Swarm intelligence is a promising field of biologically-inspired artificial intelligence, which is based on the behavioral models of social insects. This article covers Swarm Intelligence Algorithm, i.e., grasshopper optimization algorithm (GOA) which is based on the social communication nature of the grasshopper, applied to renewable energy based economic and emission dispatch problems. Based on Weibull probability density function (W-pdf), the stochastic wind speed including optimization problem is numerically solved for a 2 renewable wind energy incorporating 6 and 14 thermal units for 3 different loads. Moreover, to improve the solution superiority and convergence speed, quasi oppositional based learning (QOBL) is included with the main GOA algorithm. The performance of GOA and QOGOA is evaluated and the simulation results as well as statistical results obtained by these methods along with different other algorithms available in the literature are presented to demonstrate the validity and effectiveness of the proposed GOA and QOGOA schemes for practical applications.

[1]  Hamid R. Tizhoosh,et al.  Opposition-Based Learning: A New Scheme for Machine Intelligence , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

[2]  Manuel López-Ibáñez,et al.  Ant colony optimization , 2010, GECCO '10.

[3]  Lei Wu,et al.  Optimal coordination of wind-hydro-thermal based on water complementing wind , 2013 .

[4]  Hongjie Jia,et al.  Optimal day-ahead scheduling of integrated urban energy systems , 2016 .

[5]  Gilbert M. Masters,et al.  Renewable and Efficient Electric Power Systems: Masters/Electric Power Systems , 2004 .

[6]  Xiangning Lin,et al.  A Fault Diagnosis Method of Power Systems Based on Improved Objective Function and Genetic Algorithm-Tabu Search , 2010, IEEE Transactions on Power Delivery.

[7]  M.-R. Haghifam,et al.  ACO Based Algorithm for Distributed Generation Sources Allocation and Sizing in Distribution Systems , 2007, 2007 IEEE Lausanne Power Tech.

[8]  J.G. Vlachogiannis,et al.  Quantum-Inspired Evolutionary Algorithm for Real and Reactive Power Dispatch , 2008, IEEE Transactions on Power Systems.

[9]  Bijay Ketan Panigrahi,et al.  Hybrid flower pollination algorithm with time-varying fuzzy selection mechanism for wind integrated multi-objective dynamic economic dispatch , 2015 .

[10]  Niladri Chakraborty,et al.  Effect of Control Parameters on Differential Evolution based Combined Economic Emission Dispatch with Valve-Point Loading and Transmission Loss , 2008 .

[11]  Behrooz Vahidi,et al.  Solution of combined economic and emission dispatch problem using a novel chaotic improved harmony search algorithm , 2019, J. Comput. Des. Eng..

[12]  Fushuan Wen,et al.  Optimal Dispatch of Electric Vehicles and Wind Power Using Enhanced Particle Swarm Optimization , 2012, IEEE Transactions on Industrial Informatics.

[13]  David C. Yu,et al.  An Economic Dispatch Model Incorporating Wind Power , 2008, IEEE Transactions on Energy Conversion.

[14]  Z. Dong,et al.  Quantum-Inspired Particle Swarm Optimization for Valve-Point Economic Load Dispatch , 2010, IEEE Transactions on Power Systems.

[15]  Ali M. Eltamaly,et al.  Swarm intelligence-based optimization of grid-dependent hybrid renewable energy systems , 2017 .

[16]  A. Arabali,et al.  Cost analysis of a power system using probabilistic optimal power flow with energy storage integration and wind generation , 2013 .

[17]  Yanbin Yuan,et al.  An extended NSGA-III for solution multi-objective hydro-thermal-wind scheduling considering wind power cost , 2015 .

[18]  Serap Ulusam Seçkiner,et al.  Wind farm layout optimization using particle filtering approach , 2013 .

[19]  Doaa Khalil Ibrahim,et al.  A dynamic economic emission dispatch considering wind power uncertainty incorporating energy storage system and demand side management , 2016 .

[20]  Sakti Prasad Ghoshal,et al.  Combined economic and emission dispatch problems using biogeography-based optimization , 2010 .

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

[22]  K. Lee,et al.  A new structural optimization method based on the harmony search algorithm , 2004 .

[23]  Kit Po Wong,et al.  Quantum-Inspired Particle Swarm Optimization for Power System Operations Considering Wind Power Uncertainty and Carbon Tax in Australia , 2012, IEEE Transactions on Industrial Informatics.

[24]  Hing Kai Chan,et al.  A Two-Level Genetic Algorithm to Determine Production Frequencies for Economic Lot Scheduling Problem , 2012, IEEE Transactions on Industrial Electronics.

[25]  Ting Wu,et al.  Coordinated Energy Dispatching in Microgrid With Wind Power Generation and Plug-in Electric Vehicles , 2013, IEEE Transactions on Smart Grid.

[26]  Stefan Preitl,et al.  Fuzzy Control Systems With Reduced Parametric Sensitivity Based on Simulated Annealing , 2012, IEEE Transactions on Industrial Electronics.

[27]  Ebrahim Farjah,et al.  An efficient scenario-based and fuzzy self-adaptive learning particle swarm optimization approach for dynamic economic emission dispatch considering load and wind power uncertainties , 2013 .

[28]  Aniruddha Bhattacharya,et al.  Multi-objective economic emission load dispatch solution using gravitational search algorithm and considering wind power penetration , 2013 .

[29]  Jan K. Sykulski,et al.  Application of pattern search method to power system valve-point economic load dispatch , 2007 .

[30]  Chun Chen,et al.  An interval optimization based day-ahead scheduling scheme for renewable energy management in smart distribution systems , 2015 .

[31]  Shengwei Mei,et al.  Distributionally robust hydro-thermal-wind economic dispatch , 2016 .

[32]  P. K. Chattopadhyay,et al.  Hybrid Differential Evolution With Biogeography-Based Optimization for Solution of Economic Load Dispatch , 2010, IEEE Transactions on Power Systems.

[33]  Xiaofei Wang,et al.  Bi-level robust dynamic economic emission dispatch considering wind power uncertainty , 2016 .

[34]  Shahryar Rahnamayan,et al.  Quasi-oppositional Differential Evolution , 2007, 2007 IEEE Congress on Evolutionary Computation.

[35]  K.Y. Lee,et al.  Multi-Objective Evolutionary Programming for Economic Emission Dispatch problem , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[36]  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 .

[37]  Provas Kumar Roy,et al.  An efficient evolutionary algorithm applied to economic load dispatch problem , 2015, Proceedings of the 2015 Third International Conference on Computer, Communication, Control and Information Technology (C3IT).

[38]  M. E. Hamedani Golshan,et al.  Distributed generation, reactive sources and network-configuration planning for power and energy-loss reduction , 2006 .

[39]  Kwang Y. Lee,et al.  Modified optimal power flow on storage devices and wind power integrated system , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[40]  Provas Kumar Roy,et al.  Renewable Energy Based Economic Emission Load Dispatch Using Grasshopper Optimization Algorithm , 2019, Int. J. Swarm Intell. Res..

[41]  Bijaya K. Panigrahi,et al.  Bio-inspired optimisation for economic load dispatch: a review , 2014, Int. J. Bio Inspired Comput..

[42]  Provas Kumar Roy,et al.  Economic Load Dispatch Considering Non-smooth Cost Functions Using Predator–Prey Optimization , 2015 .

[43]  Wilsun Xu,et al.  Minimum Emission Dispatch Constrained by Stochastic Wind Power Availability and Cost , 2010, IEEE Transactions on Power Systems.