The Application of Multi-Objective Particle Swarm Optimization in Economic Dispatch of Power System

2013 ◽  
Vol 760-762 ◽  
pp. 2119-2122
Author(s):  
Peng Zheng ◽  
Wen Tan Jiao
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Power Plants ◽  
Economic Dispatch ◽  
Particle Swarm ◽  
Inequality Constraints ◽  
Pollutant Emission ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Multi Objective

Economic dispatch (ED) is a typical power system operation optimization problem. But it has non-smooth cost functions with equality and inequality constraints that make the problem of finding the global optimum difficult. According to the characteristics of economic dispatch problem, a improved algorithm based on particle swarm optimization for solving economic dispatch strategy is researched in this paper. Multi-objective economic\environmental dispatch demands that the pollutant emission of power plants should reach minimum while the condition of least generation cost should be satisfied. According to this demand, this multi-objective problem is solved by improved particle swarm optimization (PSO) algorithm. Using particle position and speed of change in the familiar update, the multi-objective particle swarm algorithm based on test function of this algorithm, and the simulation results of simulation optimization. The effectiveness of the proposed algorithm is verified by Simulation.

scholarly journals An Elitist Transposon Quantum-Based Particle Swarm Optimization Algorithm for Economic Dispatch Problems

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Angus Wu ◽  
Zhen-Lun Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Economic Dispatch ◽  
Particle Swarm ◽  
Inequality Constraints ◽  
Population Based ◽  
Performance Comparison ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Highly Nonlinear ◽  
Equality And Inequality Constraints

Population-based optimization algorithms are useful tools in solving engineering problems. This paper presents an elitist transposon quantum-based particle swarm algorithm to solve economic dispatch (ED) problems. It is a complex and highly nonlinear constrained optimization problem. The proposed approach, double elitist breeding quantum-based particle swarm optimization (DEB-QPSO), makes use of two elitist breeding strategies to promote the diversity of the swarm so as to enhance the global search ability and an improved efficient heuristic handling technique to manage the equality and inequality constraints of ED problems. Investigating on 15-unit, 40-unit, and 140-unit widely used test systems, through performance comparison, the proposed DEB-QPSO algorithm is able to obtain higher-quality solutions efficiently and stably superior than the other the state-of-the-art algorithms.

scholarly journals A Novel Multi-Objective Discrete Particle Swarm Optimization with Elitist Perturbation for Reconfiguration of Ship Power System

2017 ◽  
Vol 24 (s3) ◽  
pp. 79-85
Author(s):  
Lingjie Zhang ◽  
Jianbo Sun ◽  
Chen Guo
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Particle Swarm ◽  
Index Test ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle ◽  
Multi Objective ◽  
Shipboard Power System ◽  
Shipboard Power

Abstract A novel multi-objective discrete particle swarm optimization with elitist perturbation strategy (EPSMODPSO) is proposed and applied to solve the reconfiguration problem of shipboard power system(SPS). The new algorithm uses the velocity to decide each particle to move one step toward positive or negative direction to update the position. An elitist perturbation strategy is proposed to improve the local search ability of the algorithm. Reconfiguration model of SPS is established with multiple objectives, and an inherent homogeneity index is adopted as the auxiliary estimating index. Test results of examples show that the proposed EPSMODPSO performs excellent in terms of diversity and convergence of the obtained Pareto optimal front. It is competent to solve network reconfiguration of shipboard power system and other multi-objective discrete optimization problems.

scholarly journals An Improved Particle Swarm Optimization with Biogeography-Based Learning Strategy for Economic Dispatch Problems

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Xu Chen ◽  
Bin Xu ◽  
Wenli Du
Keyword(s):  
Particle Swarm Optimization ◽  
Learning Strategy ◽  
Economic Dispatch ◽  
Operating Cost ◽  
Particle Swarm ◽  
Inequality Constraints ◽  
Optimization Techniques ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Save Energy

Economic dispatch (ED) plays an important role in power system operation, since it can decrease the operating cost, save energy resources, and reduce environmental load. This paper presents an improved particle swarm optimization called biogeography-based learning particle swarm optimization (BLPSO) for solving the ED problems involving different equality and inequality constraints, such as power balance, prohibited operating zones, and ramp-rate limits. In the proposed BLPSO, a biogeography-based learning strategy is employed in which particles learn from each other based on the quality of their personal best positions, and thus it can provide a more efficient balance between exploration and exploitation. The proposed BLPSO is applied to solve five ED problems and compared with other optimization techniques in the literature. Experimental results demonstrate that the BLPSO is a promising approach for solving the ED problems.

Multi-Objective Optimal Dispatch of Power System with Wind Power Based on Improved Particle Swarm Algorithm

2013 ◽  
Vol 860-863 ◽  
pp. 353-360
Author(s):  
Yi Hui Zhang ◽  
Zhi Jian Hu ◽  
Xiao Lu Gong ◽  
Meng Lin Zhang ◽  
He Wang ◽  
...  
Keyword(s):  
Power System ◽  
Wind Power ◽  
Particle Swarm ◽  
Environmental Benefits ◽  
Particle Swarm Algorithm ◽  
Security Risks ◽  
Swarm Optimization ◽  
Optimal Dispatch ◽  
Multi Objective ◽  
Simulation Results

With the increase of its proportion in power system, wind power not only brings economic and environmental benefits but also the potential security risks due to its fluctuation and uncertainty. In order to take the potential security risks into account, an environmental/economic/safe static dispatch (EESD) model of power system with wind power is built. A new multi-objective particle swarm optimization (MOPSO) algorithm with standby selection and micro variation is proposed to solve the model. Two cases with and without wind power are simulated with the standard IEEE-30 system. The simulation results validate the effectiveness of the proposed algorithm.

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