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.

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.

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 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.

scholarly journals Fat Tail Model for Simulating Test Systems in Multiperiod Unit Commitment

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
J. A. Marmolejo ◽  
R. Rodriguez
Keyword(s):  
Power Systems ◽  
Unit Commitment ◽  
Electrical Power ◽  
Random Variables ◽  
Production Costs ◽  
Unit Commitment Problem ◽  
Test Systems ◽  
Fat Tail ◽  
Commitment Problem ◽  
Stable Random Variables

This paper describes the use of Chambers-Mallows-Stuck method for simulating stable random variables in the generation of test systems for economic analysis in power systems. A study that focused on generating test electrical systems through fat tail model for unit commitment problem in electrical power systems is presented. Usually, the instances of test systems in Unit Commitment are generated using normal distribution, but in this work, simulations data are based on a new method. For simulating, we used three original systems to obtain the demand behavior and thermal production costs. The estimation of stable parameters for the simulation of stable random variables was based on three generally accepted methods: (a) regression, (b) quantiles, and (c) maximum likelihood, choosing one that has the best fit of the tails of the distribution. Numerical results illustrate the applicability of the proposed method by solving several unit commitment problems.

scholarly journals A New Solution to Profit Based Unit Commitment Problem Considering PEVs/BEVs and Renewable Energy Sources

2020 ◽  
Vol 184 ◽  
pp. 01070
Author(s):  
Ayani Nandi ◽  
Vikram Kumar Kamboj
Keyword(s):  
Renewable Energy ◽  
Electric Vehicles ◽  
Production Cost ◽  
Unit Commitment ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Unit Commitment Problem ◽  
Load Demand ◽  
Commitment Problem ◽  
The Impact

Daily load demand for industrial, residential and commercial sectors are changing day by day. Also, inclusion of e-mobility has totally effected the operations of realistic power sector. Hence, to meet this time varying load demand with minimum production cost is very challenging. The proposed research work focuses on the mathematical formulation of profit based unit commitment problem of realistic power system considering the impact of battery electric vehicles, hybrid electric vehicles and plug in electric vehicles and its solution using Intensify Harris Hawks Optimizer (IHHO). The coordination of plants with each other is named as Unit commitment of plants in which the most economical patterns of the generating station is taken so as to gain low production cost with higher reliability. But with the increase in industrialization has affected the environment badly so to maintain the balance between the generation and environment a new thinking of generating low cost power with high reliability by causing less harm to environment i.e. less emission of flue gases is adopted by considering renewable energy sources.

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