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 Efficient Energy Load Distribution Model using Modified Particle Swarm Optimization Algorithm

2021 ◽  
Vol 2 (4) ◽  
pp. 226-231
Author(s):  
Vijayakumar T ◽  
Vinothkanna R
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Electric Power ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Economic Load Dispatch ◽  
Electric Power Generation ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Modified Pso

Reduction of emission and energy conservation plays a major role in the current power system for realizing sustainable socio-economic development. The application prospects and practical significance of economic load dispatch issue in the electric power market is remarkable. The various generating sets must be assigned with load capacity in a reasonable manner for reducing the cost of electric power generation. This problem may be overcome by the proposed modified particle swarm optimization (PSO) algorithm. The practical issue is converted and modelled into its corresponding mathematical counterpart by establishing certain constraints. Further, a novel interdependence strategy along with a modified PSO algorithm is implemented for balancing the local search capability and global optimization. Multiple swarms are introduced in the modified PSO algorithm. Certain standard test functions are executed for specific analysis. Finally, the proposed modified PSO algorithm can optimize the economic load dispatch problem while saving the energy resources to a larger extent. The algorithm evaluation can be performed using real-time examples for verifying the efficiency. When compared to existing schemes like artificial bee colony (ABC), genetic algorithms (GAs), and conventional PSO algorithms, the proposed scheme offers lowest electric power generation cost and overcomes the load dispatch issue according to the simulation results.

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 Wind Farm Layout Optimization with Different Hub Heights in Manjil Wind Farm Using Particle Swarm Optimization

2021 ◽  
Vol 11 (20) ◽  
pp. 9746
Author(s):  
Menova Yeghikian ◽  
Abolfazl Ahmadi ◽  
Reza Dashti ◽  
Farbod Esmaeilion ◽  
Alireza Mahmoudan ◽  
...  
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Wind Turbines ◽  
Wind Farm ◽  
Particle Swarm ◽  
Primary Objective ◽  
Wake Effect ◽  
Optimal Arrangement ◽  
Swarm Optimization ◽  
Wind Farm Layout

Nowadays, optimizing wind farm configurations is one of the biggest concerns for energy communities. The ongoing investigations have so far helped increasing power generation and reducing corresponding costs. The primary objective of this study is to optimize a wind farm layout in Manjil, Iran. The optimization procedure aims to find the optimal arrangement of this wind farm and the best values for the hubs of its wind turbines. By considering wind regimes and geographic data of the considered area, and using the Jensen’s method, the wind turbine wake effect of the proposed configuration is simulated. The objective function in the optimization problem is set in such a way to find the optimal arrangement of the wind turbines as well as electricity generation costs, based on the Mossetti cost function, by implementing the particle swarm optimization (PSO) algorithm. The results reveal that optimizing the given wind farm leads to a 10.75% increase in power generation capacity and a 9.42% reduction in its corresponding cost.

scholarly journals Particle swarm optimization with constriction factor for optimal reactive power dispatch

2013 ◽  
Vol 16 (2) ◽  
pp. 89-101
Author(s):  
Dieu Ngoc Vo ◽  
Dung Anh Le ◽  
Tu Phan Vu
Keyword(s):  
Particle Swarm Optimization ◽  
Voltage Stability ◽  
Reactive Power ◽  
Particle Swarm ◽  
Total Power ◽  
Swarm Optimization ◽  
Optimal Reactive Power Dispatch ◽  
Power Dispatch ◽  
Reactive Power Dispatch ◽  
Constriction Factor

This paper proposes a simple particle swarm optimization with constriction factor (PSO-CF) method for solving optimal reactive power dispatch (ORPD) problem. The proposed PSO-CF is the conventional particle swarm optimization based on constriction factor which can deal with different objectives of the problem such as minimizing the real power losses, improving the voltage profile, and enhancing the voltage stability and properly handle various constraints for reactive power limits of generators and switchable capacitor banks, bus voltage limits, tap changer limits for transformers, and transmission line limits. The proposed method has been tested on the IEEE 30-bus and IEEE 118-bus systems and the obtained results are compared to those from other PSO variants and other methods in the literature. The result comparison has shown that the proposed method can obtain total power loss, voltage deviation or voltage stability index less than the others for the considered cases. Therefore, the proposed PSO-CF can be favorable solving the ORPD problem.

scholarly journals An Improved Symbiosis Particle Swarm Optimization for Solving Economic Load Dispatch Problem

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jianxia Zhang ◽  
Jianxin Zhang ◽  
Feng Zhang ◽  
Minglu Chi ◽  
Linbin Wan
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Electric Power ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Economic Load Dispatch ◽  
Electric Power Generation ◽  
Swarm Optimization ◽  
Symbiotic Organisms Search

To realize the sustainable development of social economy, energy conservation and emission reduction has become one of the problems that must be considered in the current power system. Under the electric power market system, the economic load dispatch problem not only is important but also has practical significance and broad application prospects. In order to minimize the costs of electric-power generation, the load capacity should be reasonably assigned among many different generating sets. In this paper, an improved symbiosis particle swarm optimization algorithm was proposed, aiming at providing a better solution to this problem. First of all, a mathematical model was established with certain constraints, which successfully converted the practical problem into a mathematical one. Then, to balance the global optimization and local search capability, an improved symbiosis particle swarm optimization algorithm with mutualistic symbiosis strategy in nature was presented. The improved symbiosis particle swarm optimization algorithm consisted of three swarms inspired by the proverb “two heads are better than one,” and its specific analysis was through the standard test functions. At last, the economic load dispatch problem could be optimized by the proposed improved symbiosis particle swarm optimization algorithm. In addition, two different kinds of practical examples were also adopted for algorithm evaluation. From the simulation results, it can be seen clearly that the costs of electric-power generation gained were the lowest compared with the results of particle swarm optimization algorithm, improved chaos particle swarm optimization algorithm, and symbiotic organisms search algorithm, well demonstrating the effectiveness of the improved symbiosis particle swarm optimization algorithm in solving the economic load dispatch problem.

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