Economic load dispatch using novel bat algorithm with quantum and mechanical behaviour

2017 ◽  
Author(s):  
Hafiz Tehzeeb ul Hassan ◽  
Muhammad Usman Asghar ◽  
Muhammad Zunair Zamir ◽  
Hafiz M. Aamir Faiz
Keyword(s):  
Mechanical Behaviour ◽  
Bat Algorithm ◽  
Economic Load Dispatch

scholarly journals A Cauchy-Gaussian Quantum-Behaved Bat Algorithm Applied to Solve the Economic Load Dispatch Problem

IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Francois Xavier Rugema ◽  
Gangui Yan ◽  
Sylvere Mugemanyi ◽  
Qi Jia ◽  
Shanfeng Zhang ◽  
...  
Keyword(s):  
Bat Algorithm ◽  
Economic Load Dispatch

scholarly journals Solving Renewables-Integrated Economic Load Dispatch Problem by Variant of Metaheuristic Bat-Inspired Algorithm

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6225
Author(s):  
Faisal Tariq ◽  
Salem Alelyani ◽  
Ghulam Abbas ◽  
Ayman Qahmash ◽  
Mohammad Rashid Hussain
Keyword(s):  
Power Generation ◽  
Renewable Energy Sources ◽  
Bat Algorithm ◽  
Optimization Techniques ◽  
Computational Time ◽  
Economic Load Dispatch ◽  
Good Convergence ◽  
Load Demand ◽  
Metaheuristic Techniques ◽  
Operational Constraints

One of the most important concerns in the planning and operation of an electric power generation system is the effective scheduling of all power generation facilities to meet growing power demand. Economic load dispatch (ELD) is a phenomenon where an optimal combination of power generating units is selected in such a way as to minimize the total fuel cost while satisfying the load demand, subject to operational constraints. Different numerical and metaheuristic optimization techniques have gained prominent importance and are widely used to solve the nonlinear problem. Although metaheuristic techniques have a good convergence rate than numerical techniques, however, their implementation seems difficult in the presence of nonlinear and dynamic parameters. This work is devoted to solving the ELD problem with the integration of variable energy resources using a modified directional bat algorithm (dBA). Then the proposed technique is validated via different realistic test cases consisting of thermal and renewable energy sources (RESs). From simulation results, it is observed that dBA reduces the operational cost with less computational time and has better convergence characteristics than that of standard BA and other popular techniques like particle swarm optimization (PSO) and genetic algorithm (GA).

scholarly journals Modified Particle Swarm Optimisation for Economic Load Dispatch Problem

2019 ◽  
Vol 8 (2S3) ◽  
pp. 172-177
Keyword(s):  
Particle Swarm ◽  
Solution Space ◽  
Bat Algorithm ◽  
Steady Increase ◽  
Cost Curve ◽  
Economic Load Dispatch ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Cost Parameters ◽  
The Cost

For every Power System the concept of Economic Load Dispatch has and continues to be an area of immense importance and concern. With the steady increase in demand over the years it has become all the more mandatory to reduce the cost of power generation and at the same time meet all the power requirements of the concerned area. There are a number of techniques to solve the ever existing problem of Economic Load Dispatch such as Bat Algorithm, Firefly Method, NewtonRaphson Method, Lambda Iterative Method and so on and so forth. Each of these heuristic approaches require the cost curve to be piece wise linear. In this paper, Modified Particle Swarm Optimization has been proposed to find the most economical and ideal solution to the load dispatch problem. This is done by focussing on the population size of the solution space more than on other cost parameters. The simulation for the system is performed on MATLAB software

Quantum-Behaved Bat Algorithm for Solving the Economic Load Dispatch Problem Considering a Valve-Point Effect

2021 ◽  
pp. 93-110
Author(s):  
Pandian Vasant ◽  
Fahad Parvez Mahdi ◽  
Jose Antonio Marmolejo-Saucedo ◽  
Igor Litvinchev ◽  
Roman Rodriguez Aguilar ◽  
...  
Keyword(s):  
Nonlinear Optimization ◽  
Optimization Problems ◽  
Bat Algorithm ◽  
Evolutionary Programming ◽  
Economic Load Dispatch ◽  
Solution Quality ◽  
Swarm Optimization ◽  
Real World Problem ◽  
Valve Point Effect ◽  
Power Generation Systems

Quantum computing-inspired metaheuristic algorithms have emerged as a powerful computational tool to solve nonlinear optimization problems. In this paper, a quantum-behaved bat algorithm (QBA) is implemented to solve a nonlinear economic load dispatch (ELD) problem. The objective of ELD is to find an optimal combination of power generating units in order to minimize total fuel cost of the system, while satisfying all other constraints. To make the system more applicable to the real-world problem, a valve-point effect is considered here with the ELD problem. QBA is applied in 3-unit, 10-unit, and 40-unit power generation systems for different load demands. The obtained result is then presented and compared with some well-known methods from the literature such as different versions of evolutionary programming (EP) and particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), simulated annealing (SA) and hybrid ABC_PSO. The comparison of results shows that QBA performs better than the above-mentioned methods in terms of solution quality, convergence characteristics and computational efficiency. Thus, QBA proves to be an effective and a robust technique to solve such nonlinear optimization problem.

Quantum-Behaved Bat Algorithm for Solving the Economic Load Dispatch Problem Considering a Valve-Point Effect

2020 ◽  
Vol 11 (3) ◽  
pp. 41-57
Author(s):  
Pandian Vasant ◽  
Fahad Parvez Mahdi ◽  
Jose Antonio Marmolejo-Saucedo ◽  
Igor Litvinchev ◽  
Roman Rodriguez Aguilar ◽  
...  
Keyword(s):  
Nonlinear Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Bat Algorithm ◽  
Economic Load Dispatch ◽  
Solution Quality ◽  
Real World Problem ◽  
Valve Point Effect ◽  
Power Generation Systems ◽  
Better Than

Quantum computing-inspired metaheuristic algorithms have emerged as a powerful computational tool to solve nonlinear optimization problems. In this paper, a quantum-behaved bat algorithm (QBA) is implemented to solve a nonlinear economic load dispatch (ELD) problem. The objective of ELD is to find an optimal combination of power generating units in order to minimize total fuel cost of the system, while satisfying all other constraints. To make the system more applicable to the real-world problem, a valve-point effect is considered here with the ELD problem. QBA is applied in 3-unit, 10-unit, and 40-unit power generation systems for different load demands. The obtained result is then presented and compared with some well-known methods from the literature such as different versions of evolutionary programming (EP) and particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), simulated annealing (SA) and hybrid ABC_PSO. The comparison of results shows that QBA performs better than the above-mentioned methods in terms of solution quality, convergence characteristics and computational efficiency. Thus, QBA proves to be an effective and a robust technique to solve such nonlinear optimization problem.

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