scholarly journals GWO-SA: A Novel Hybrid Grey Wolf Optimizer-Simulated Annealing algorithm for Multidisciplinary Design Optimization Problems

2019 ◽  
Vol 8 (4) ◽  
pp. 1279-1299 ◽  
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Unit Commitment ◽  
Electric Power System ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Unit Commitment Problem ◽  
Spinning Reserve ◽  
Commitment Problem ◽  
Annealing Algorithm

The improved variants of Grey wolf optimizer has good exploration capability for global optimum solution. However, the exploitation competence of the existing variants of grey wolf optimizer is very poor. Researchers are continuously trying to improve the exploitation phase of the existing grey wolf optimizer, but still the improved variants of grey wolf optimizer are lacking in local search capability. In the proposed research, the exploitation phase of the existing grey wolf optimizer has been further improved using simulated annealing algorithm and the proposed hybrid optimizer has been named as hGWO-SA algorithm. The effectiveness of the proposed hybrid variant has been tested for various benchmark problems including multi-disciplinary optimization and design engineering problems and unit commitment problem of electric power system and it has been experimentally found that the proposed optimizer performs much better than existing variants of grey wolf optimizer. The feasibility of hGWO-SA algorithm has been tested for small & medium scale power systems unit commitment problem. In which, the results for 4 unit, 5 unit, 6 unit, 7 unit, 10 units, 19 unit, 20 unit, 40 unit and 60 units are evaluated. The 10-generating units are evaluated with 5% and 10% spinning reserve. The results obviously show that the suggested method gives the superior type of solutions as compared to other algorithms.

hGWO-SA: A Novel Hybrid Grey Wolf Optimizer-Simulated Annealing algorithm for Engineering and Power System Optimization Problems

2020 ◽  
Vol 13 ◽  
Author(s):  
Vikram Kumar Kamboj
Keyword(s):  
Simulated Annealing ◽  
Power System ◽  
Simulated Annealing Algorithm ◽  
Unit Commitment ◽  
System Optimization ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Unit Commitment Problem ◽  
Commitment Problem ◽  
Annealing Algorithm

: The improved variants of Grey wolf optimizer has good exploration capability for global optimum solution. However, the exploitation competence of the existing variants of grey wolf optimizer is unfortunate. Researchers are continuously trying to improve the exploitation phase of the existing grey wolf optimizer, but still the improved variants of grey wolf optimizer are lacking in local search capability. In the proposed research, the exploitation phase of the existing grey wolf optimizer has been further improved using simulated annealing algorithm and the proposed hybrid optimizer has been named as hGWO-SA algorithm. The effectiveness of the proposed hybrid variant has been tested for various benchmark problems including multi-disciplinary optimization and design engineering problems and unit commitment problem of electric power system and it has been experimentally found that the proposed optimizer performs much better than existing variants of grey wolf optimizer. The feasibility of hGWO-SA algorithm has been tested for small & medium scale power systems unit commitment problem. In which, the results for 4 unit, 5 unit, 6 unit, 7 unit, 10 units, 19 unit, 20 unit, 40 unit and 60 units are evaluated. The 10-generating units are evaluated with 5% and 10% spinning reserve. The results obviously show that the suggested method gives the superior type of solutions as compared to other algorithms.

scholarly journals A meliorated Harris Hawks optimizer for combinatorial unit commitment problem with photovoltaic applications

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ayani Nandi ◽  
Vikram Kumar Kamboj
Keyword(s):  
Power Generation ◽  
Power System ◽  
Electric Power ◽  
Unit Commitment ◽  
Renewable Energy Sources ◽  
Electric Power System ◽  
Unit Commitment Problem ◽  
Spinning Reserve ◽  
Commitment Problem ◽  
Sine Cosine Algorithm

AbstractConventional unit commitment problem (UCP) consists of thermal generating units and its participation schedule, which is a stimulating and significant responsibility of assigning produced electricity among the committed generating units matter to frequent limitations over a scheduled period view to achieve the least price of power generation. However, modern power system consists of various integrated power generating units including nuclear, thermal, hydro, solar and wind. The scheduling of these generating units in optimal condition is a tedious task and involves lot of uncertainty constraints due to time carrying weather conditions. This difficulties come to be too difficult by growing the scope of electrical power sector day by day, so that UCP has connection with problem in the field of optimization, it has both continuous and binary variables which is the furthermost exciting problem that needs to be solved. In the proposed research, a newly created optimizer, i.e., Harris Hawks optimizer (HHO), has been hybridized with sine–cosine algorithm (SCA) using memetic algorithm approach and named as meliorated Harris Hawks optimizer and it is applied to solve the photovoltaic constrained UCP of electric power system. In this research paper, sine–cosine Algorithm is used for provision of power generation (generating units which contribute in electric power generation for upload) and economic load dispatch (ELD) is completed by Harris Hawks optimizer. The feasibility and efficacy of operation of the hybrid algorithm are verified for small, medium power systems and large system considering renewable energy sources in summer and winter, and the percentage of cost saving for power generation is found. The results for 4 generating units, 5 generating units, 6 generating units, 7 generating units, 10 generating units, 19 generating units, 20 generating units, 40 generating units and 60 generating units are evaluated. The 10 generating units are evaluated with 5% and 10% spinning reserve. The efficacy of the offered optimizer has been verified for several standard benchmark problem including unit commitment problem, and it has been observed that the suggested optimizer is too effective to solve continuous, discrete and nonlinear optimization problems.

Binary Grey Wolf Optimizer for large scale unit commitment problem

2018 ◽  
Vol 38 ◽  
pp. 251-266 ◽  
Author(s):  
Lokesh Kumar Panwar ◽  
Srikanth Reddy K ◽  
Ashu Verma ◽  
B.K. Panigrahi ◽  
Rajesh Kumar
Keyword(s):  
Large Scale ◽  
Unit Commitment ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Unit Commitment Problem ◽  
Scale Unit ◽  
Commitment Problem

Meta-heuristic framework: Quantum inspired binary grey wolf optimizer for unit commitment problem

2018 ◽  
Vol 70 ◽  
pp. 243-260 ◽  
Author(s):  
K Srikanth ◽  
Lokesh Kumar Panwar ◽  
BK Panigrahi ◽  
Enrique Herrera-Viedma ◽  
Arun Kumar Sangaiah ◽  
...  
Keyword(s):  
Unit Commitment ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Unit Commitment Problem ◽  
Commitment Problem

scholarly journals The optimal solution for unit commitment problem using binary hybrid grey wolf optimizer

2022 ◽  
Vol 12 (1) ◽  
pp. 122
Author(s):  
Ali Iqbal Abbas ◽  
Afaneen Anwer
Keyword(s):  
Power Systems ◽  
Production Cost ◽  
Unit Commitment ◽  
Optimal Solution ◽  
Suggested Method ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Unit Commitment Problem ◽  
Commitment Problem ◽  
Quadratic Programming Method

The aim of this work is to solve the unit commitment (UC) problem in power systems by calculating minimum production cost for the power generation and finding the best distribution of the generation among the units (units scheduling) using binary grey wolf optimizer based on particle swarm optimization (BGWOPSO) algorithm. The minimum production cost calculating is based on using the quadratic programming method and represents the global solution that must be arriving by the BGWOPSO algorithm then appearing units status (on or off). The suggested method was applied on “39 bus IEEE test systems”, the simulation results show the effectiveness of the suggested method over other algorithms in terms of minimizing of production cost and suggesting excellent scheduling of units.

A Simulated Annealing Based Hybrid Optimization Approach for a Fuzzy Modelled Unit Commitment Problem

2005 ◽  
Vol 3 (1) ◽  
Author(s):  
Aruldoss T Albert Victoire ◽  
Ebenezer A Jeyakumar
Keyword(s):  
Fuzzy Logic ◽  
Simulated Annealing ◽  
Unit Commitment ◽  
Fitness Function ◽  
Optimization Approach ◽  
Perturbation Scheme ◽  
Unit Commitment Problem ◽  
Spinning Reserve ◽  
Simple Method ◽  
Commitment Problem

This article presents a solution model for the unit commitment problem (UCP) using fuzzy logic to address uncertainties in the problem. Hybrid simulated annealing (SA), particle swarm optimization (PSO) and sequential quadratic programming (SQP) technique (hybrid SA-PSO-SQP) is used to schedule the generating units based on the fuzzy logic decisions. The fitness function for the hybrid SA-PSO-SQP is formulated by combining the objective function of UCP and a penalty calculated from the fuzzy logic decisions. Fuzzy decisions are made based on the statistics of the load demand error and spinning reserve maintained at each hour. SA is used to solve the combinatorial sub-problem of the UCP. An improved random perturbation scheme and a simple method for generating initial feasible commitment schedule are proposed for the SA method. The non-linear programming sub-problem of the UCP is solved using the hybrid PSO-SQP technique. Simulation results on a practical Neyveli Thermal Power Station system (NTPS) in India and several example systems validate, the presented UCP model is reasonable by ensuring quality solution with sufficient level of spinning reserve throughout the scheduling horizon for secure operation of the system.

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