scholarly journals Economic Load Dispatch Problem Based Biogeography Algorithm

2012 ◽  
pp. 92-97
Author(s):  
K.J.Vishnu Sudhan ◽  
R.S Saravana Kumar ◽  
A. Rathina Grace Monica
Keyword(s):  
Model Problem ◽  
Optimization Methods ◽  
Global Optimum ◽  
Economic Load Dispatch ◽  
Power Demand ◽  
Swarm Optimization ◽  
Quadratic Cost ◽  
Generation Cost ◽  
Thermal Generator ◽  
Quadratic Cost Function

In Recent Scenario, scarcity of energy source, ever growing Power demand, increasing in generation cost necessitate optimal economic dispatch. This paper includes Biogeography Algorithm to compute Economic Load Dispatch Problem for Thermal generator of power system. Biogeography Describes how the species arise, migrates from one habitat another and gets wipes out.In BGA model, problem solutions are represents as islands and sharing of features between solution is represented as immigration and emigration which searches for the global optimum mainly through two steps :migration and mutation. BGA has features in common with other biology-based optimization methods, such as GAs and particle swarm optimization (PSO). This makes BGA applicable to many of the same types of problems that GAs and PSO are used for, namely, high-dimension problems with multiple local optimal. To show the advantages of the proposed algorithm, it has been applied to two different test systems for solving ELD problems. First, a 3-generators system then a 6 generators system with simple quadratic cost function considering operating limit constraints is considered.

scholarly journals An Adaptive Particle Swarm Optimization Algorithm for Unconstrained Optimization

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Feng Qian ◽  
Mohammad Reza Mahmoudi ◽  
Hamïd Parvïn ◽  
Kim-Hung Pho ◽  
Bui Anh Tuan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Global Optimum ◽  
Initial Population ◽  
Swarm Optimization ◽  
Conventional Optimization

Conventional optimization methods are not efficient enough to solve many of the naturally complicated optimization problems. Thus, inspired by nature, metaheuristic algorithms can be utilized as a new kind of problem solvers in solution to these types of optimization problems. In this paper, an optimization algorithm is proposed which is capable of finding the expected quality of different locations and also tuning its exploration-exploitation dilemma to the location of an individual. A novel particle swarm optimization algorithm is presented which implements the conditioning learning behavior so that the particles are led to perform a natural conditioning behavior on an unconditioned motive. In the problem space, particles are classified into several categories so that if a particle lies within a low diversity category, it would have a tendency to move towards its best personal experience. But, if the particle’s category is with high diversity, it would have the tendency to move towards the global optimum of that category. The idea of the birds’ sensitivity to its flying space is also utilized to increase the particles’ speed in undesired spaces in order to leave those spaces as soon as possible. However, in desirable spaces, the particles’ velocity is reduced to provide a situation in which the particles have more time to explore their environment. In the proposed algorithm, the birds’ instinctive behavior is implemented to construct an initial population randomly or chaotically. Experiments provided to compare the proposed algorithm with the state-of-the-art methods show that our optimization algorithm is one of the most efficient and appropriate ones to solve the static optimization problems.

scholarly journals Economic load dispatch solutions considering multiple fuels for thermal units and generation cost of wind turbines

2021 ◽  
Vol 11 (5) ◽  
pp. 3718
Author(s):  
Anh Tuan Doan ◽  
Dinh Thanh Viet ◽  
Minh Quan Duong
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Wind Farm ◽  
Particle Swarm ◽  
Wind Farms ◽  
Total Power ◽  
Economic Load Dispatch ◽  
Swarm Optimization ◽  
Generation Cost ◽  
Constriction Factor

In this paper, economic load dispatch (ELD) problem is solved by applying a suggested improved particle swarm optimization (IPSO) for reaching the lowest total power generation cost from wind farms (WFs) and thermal units (TUs). The suggested IPSO is the modified version of Particle swarm optimization (PSO) by changing velocity and position updates. The five best solutions are employed to replace the so-far best position of each particle in velocity update mechanism and the five best solutions are used to replace previous position of each particle in position update. In addition, constriction factor is also used in the suggested IPSO. PSO, constriction factor-based PSO (CFPSO) and bat optimization algorithm (BOA) are also run for comparisons. Two systems are used to run the four methods. The first system is comprised of nine TUs with multiple fuels and one wind farm. The second system is comprised of eight TUs with multiple fuels and two WFs. From the comparisons of results, IPSO is much more powerful than three others and it can find optimal power generation with the lowest total power generation cost.

scholarly journals Optimal unit commitment of a power plant using particle swarm optimization approach

2020 ◽  
Vol 10 (2) ◽  
pp. 1135
Author(s):  
Boniface O. Anyaka ◽  
J. Felix Manirakiza ◽  
Kenneth C. Chike ◽  
Prince A. Okoro
Keyword(s):  
Particle Swarm Optimization ◽  
Power Plant ◽  
Unit Commitment ◽  
Thermal Power ◽  
Particle Swarm ◽  
Economic Load Dispatch ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Total Load ◽  
Generation Cost

Economic load dispatch among generating units is very important for any power plant. In this work, the economic load dispatch was made at Egbin Thermal Power plant supplying a total load of 600MW using six generating units. In carrying out this study, transmission losses were assumed to be included into the load supplied. Also three different combinations in the form of 6, 5 and 4 units commitment were considered. In each case, the total load was optimally dispatched between committed generating units using Particle Swarm Optimization (PSO). Similarly, the generation cost for each generating unit was determined. For case 1, the six generators were committed and the generation cost is 2,100,685.069$/h. For case 2, five generators were committed and the generation cost is 2,520,861.947$/h. For case 3, four generators were committed and the generation cost is 3,150,621.685$/h. From all considered cases, it was found that, the minimum generation cost was achieved when all six generating units were committed and a total of 420,178.878$/h was saved.

scholarly journals Optimal power scheduling for economic dispatch using moth flame optimizer

2020 ◽  
Vol 20 (1) ◽  
pp. 379
Author(s):  
N. A. M. Kamari ◽  
M. A. Zulkifley ◽  
N. F. Ramli ◽  
I. Musirin
Keyword(s):  
Power System ◽  
Power Loss ◽  
Economic Dispatch ◽  
Evolutionary Programming ◽  
Power Demand ◽  
Swarm Optimization ◽  
Total Cost ◽  
Power Scheduling ◽  
Optimal Power ◽  
Generation Cost

<p>This paper proposes the optimal generator allocation to solve Economic Dispatch (ED) problem in power system using Moth Flame Optimizer (MFO). With this approach, the optimum power for each unit generating in the system will be searched based on the power constraints per unit and the amount of power demand. The objective function of this study is to minimize the total cost of generation. The amount of power loss is measured to determine the effectiveness of the proposed technique. The performance of the MFO technique is also compared to the evolutionary programming (EP) and Particle Swarm Optimization (PSO) methods. Five- and thirty-bus power system networks are selected as test systems and simulated using MATLAB. Based on simulation results, MFO provides better results in regulating the optimum power generation with minimum generation cost and power loss, compared to EP and PSO.</p>

scholarly journals Automatic differentiation based for particle swarm optimization Steepest descent direction

2015 ◽  
Vol 1 (2) ◽  
pp. 90
Author(s):  
Aris Thobirin ◽  
Iwan Tri Riyadi Yanto
Keyword(s):  
Local Search ◽  
Automatic Differentiation ◽  
Optimization Methods ◽  
Global Optimum ◽  
Descent Direction ◽  
Benchmark Function ◽  
Swarm Optimization ◽  
Combination Methods ◽  
Gradient Based ◽  
Search Capability

Particle swam optimization (PSO) is one of the most effective optimization methods to find the global optimum point. In other hand, the descent direction (DD) is the gradient based method that has the local search capability. The combination of both methods is promising and interesting to get the method with effective global search capability and efficient local search capability. However, In many application, it is difficult or impossible to obtain the gradient exactly of an objective function. In this paper, we propose Automatic differentiation (AD) based for PSODD. we compare our methods on benchmark function. The results shown that the combination methods give us a powerful tool to find the solution.

Chaos Glowworm Swarm Optimization Algorithm Based on Cloud Model for Face Recognition

2020 ◽  
Vol 34 (12) ◽  
pp. 2056009
Author(s):  
Guangyu Zhou ◽  
Aijia Ouyang ◽  
Yuming Xu
Keyword(s):  
Face Recognition ◽  
Cloud Model ◽  
Cuckoo Search ◽  
Global Optimum ◽  
Convergence Speed ◽  
Invasive Weed ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization ◽  
Glowworm Swarm Optimization ◽  
Better Than

To overcome the shortcomings of the basic glowworm swarm optimization (GSO) algorithm, such as low accuracy, slow convergence speed and easy to fall into local minima, chaos algorithm and cloud model algorithm are introduced to optimize the evolution mechanism of GSO, and a chaos GSO algorithm based on cloud model (CMCGSO) is proposed in the paper. The simulation results of benchmark function of global optimization show that the CMCGSO algorithm performs better than the cuckoo search (CS), invasive weed optimization (IWO), hybrid particle swarm optimization (HPSO), and chaos glowworm swarm optimization (CGSO) algorithm, and CMCGSO has the advantages of high accuracy, fast convergence speed and strong robustness to find the global optimum. Finally, the CMCGSO algorithm is used to solve the problem of face recognition, and the results are better than the methods from literatures.

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