Optimization of biomass fuelled systems for distributed power generation using Particle Swarm Optimization

2008 ◽  
Vol 78 (8) ◽  
pp. 1448-1455 ◽  
Author(s):  
P. Reche López ◽  
M. Gómez González ◽  
N. Ruiz Reyes ◽  
F. Jurado
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Distributed Power ◽  
Distributed Power Generation

scholarly journals Optimal Allocation of Multiple Distributed Generations including Uncertainties in Distribution Networks by k-Means Clustering and Particle Swarm Optimization Algorithms

2021 ◽  
Vol 19 ◽  
pp. 79-84
Author(s):  
Onur Hakki Eyüboglu ◽  
◽  
Ömer Ömer Gül
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Optimal Allocation ◽  
Particle Swarm ◽  
Distribution Networks ◽  
Power Losses ◽  
Swarm Optimization ◽  
Distributed Generations ◽  
Distributed Power Generation ◽  
The One

Climate change is the one of the most important issues faced globally and reasons of it must be reduced immediately in every area. Installing distributed power generation (DG) is one of the powerful options for reducing carbon emissions in power generation. However, improper allocation of these assets has several drawbacks. Efficient, novel and robust algorithm which is combination of both k-Means clustering and Particle Swarm Optimization is proposed in order to allocate DGs. Proposed algorithm clusters distribution network buses and selects to most proper cluster to allocate DG in this way it reduces possible buses. Furthermore, sizing and generation constraints of DGs are quite important for allocation. Therefore, several cases including different DG sizes and types are implemented to obtain the best results. Moreover, multiple DG cases are included in the study. Finally, DGs have considered as wind turbines for best cases and cases have analysed in 24 hourly bases including uncertainties both demand and production side. 33 Bus test feeder power losses are reduced up to 69%, 86%, 90% at best cases and 39%, 53%, 55% at including uncertainties by proposed algorithm for cases 1, 2, 3 DG installed, respectively.

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 A Relative Analysis of Genetic Algorithm and Binary Particle Swarm Optimization for Finding the Optimal Cost of Wind Power Generation in Tirumala Area of India

2021 ◽  
Vol 40 ◽  
pp. 03016
Author(s):  
Prasun Bhattacharjee ◽  
Rabin K. Jana ◽  
Somenath Bhattacharya
Keyword(s):  
Genetic Algorithm ◽  
Power Generation ◽  
Particle Swarm Optimization ◽  
Wind Power ◽  
Greenhouse Gas Emission ◽  
Particle Swarm ◽  
Wind Power Generation ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Optimal Cost

Although India presently holds the global fourth-biggest instated Wind Power Generation (WPG) capability, it necessitates advancing more rapidly to satisfy the rising energy requirement of its evolving economy while restraining the consequential greenhouse gas emission. To accomplish the impressive target of setting up 140 GW WPG competence by 2030 as proposed by the Government of India, a greater number of financially viable wind farms are required to function all over the country without further ado. This paper focuses on finding the optimal cost for WPG in the Tirumala area of Andhra Pradesh. Genetic Algorithm (GA) and Binary Particle Swarm Optimization (BPSO) have been employed concurrently with four randomly chosen terrain conditions. The research outcomes demonstrate the superior capability of BPSO to attain the most optimal cost of energy.

scholarly journals An Improved Bare Bone Multi-Objective Particle Swarm Optimization Algorithm for Solar Thermal Power Plants

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4480
Author(s):  
Qun Niu ◽  
Han Wang ◽  
Ziyuan Sun ◽  
Zhile Yang
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Thermal Power ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Solar Thermal Power ◽  
Bare Bone

Solar energy has many advantages, such as being abundant, clean and environmentally friendly. Solar power generation has been widely deployed worldwide as an important form of renewable energy. The solar thermal power generation is one of a few popular forms to utilize solar energy, yet its modelling is a complicated problem. In this paper, an improved bare bone multi-objective particle swarm optimization algorithm (IBBMOPSO) is proposed based on the bare bone multi-objective particle swarm optimization algorithm (BBMOPSO). The algorithm is first tested on a set of benchmark problems, confirming its efficacy and the convergency speed. Then, it is applied to optimize two typical solar power generation systems including the solar Stirling power generation and the solar Brayton power generation; the results show that the proposed algorithm outperforms other algorithms for multi-objective optimization problems.

scholarly journals Research on Microgrid Optimization Based on Simulated Annealing Particle Swarm Optimization

2019 ◽  
Vol 118 ◽  
pp. 01038
Author(s):  
Shuyi Li ◽  
Xifeng Zhou ◽  
Qiangang Guo
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Simulated Annealing Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Storage Device ◽  
Swarm Optimization

Based on the pursuit of different goals in the operation of the microgrid, it is not possible to meet the lowest cost and the least pollution at the same time. From the perspective of economy and environmental protection, a microgrid model including photovoltaic power generation, wind power generation, micro gas turbine, fuel cell and energy storage device is proposed. This paper establishes a comprehensive benefit objective function that considers both microgrid fuel cost, maintenance management cost, depreciation cost, interaction cost with public grid and pollutant treatment cost. In order to avoid the defect that the traditional particle swarm optimization algorithm is easy to fall into the local optimal solution, this paper uses the combination of simulated annealing algorithm and particle swarm optimization algorithm to compare with the traditional particle swarm optimization algorithm to obtain a more suitable method for microgrid operation. Finally, a typical microgrid in China is taken as an example to verify the feasibility of the algorithm.

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.

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