Economic Load Dispatch Incorporating Wind Power Using Hybrid Biogeography-Based Optimization

This article represents salp swarm algorithm (SSA) for the most favourable operating solution of economic load dispatch (ELD). For making the convergence first along with SSA, another optimization algorithm (i.e., BBO [biogeography;based optimization]) is also used. For lowering the operational cost, wind power is employed with thermal units. SSA is inspired by swarming behaviour of salp, which belongs to salpiside family. Salp possess a special kind of swarm while hunting for food and navigating. The recommended algorithm is executed on two systems of SIX units and 40 units. In both of the cases, load dispatch problem is carried out with renewable sources and also without renewable sources. Individually, BBO, SSA, and hybrid BBO-SSA are applied to all the test systems to justify effectiveness of hybrid BBO-SSA. Obtained results assure the prospective and advantages of recommended algorithm in contrast to algorithms mentioned in the article. Results come out to be very satisfying and reveal that hybrid BBO-SSA is a powerful algorithm to solve ELD problems.

[1]  P. K. Chattopadhyay,et al.  Hybrid differential evolution with biogeography-based optimization algorithm for solution of economic emission load dispatch problems , 2011, Expert Syst. Appl..

[2]  T. Aruldoss Albert Victoire,et al.  A bio-geography-based algorithm for optimal siting and sizing of distributed generators with an effective power factor model , 2018, Comput. Electr. Eng..

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

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

[5]  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.

[6]  L. Madin.  Aspects of jet propulsion in salps , 1990 .

[7]  David E. Goldberg,et al.  Genetic algorithms and Machine Learning , 1988, Machine Learning.

[8]  S. Roy.  Market Constrained Optimal Planning for Wind Energy Conversion Systems over Multiple Installation Sites , 2002 .

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

[10]  Xin Xin,et al.  A switched dynamical system approach towards the economic dispatch of renewable hybrid power systems , 2018 .

[11]  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 .

[12]  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..

[13]  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.

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

[15]  Sanjoy Mandal,et al.  Economic load dispatch using krill herd algorithm , 2014 .

[16]  Nicanor Quijano,et al.  Distributed model predictive control for economic dispatch of power systems with high penetration of renewable energy resources , 2019 .

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

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

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

[20]  Abdullah K. Alqallaf,et al.  Solving non-convex economic load dispatch problem via artificial cooperative search algorithm , 2019, Expert Syst. Appl..

[21]  Provas Kumar Roy,et al.  Multi-objective quasi-oppositional teaching learning based optimization for economic emission load dispatch problem , 2013 .

[22]  Provas Kumar Roy,et al.  Quasi-oppositional Biogeography-based Optimization for Multi-objective Optimal Power Flow , 2011 .

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

[24]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..