OPTIMAL CONGESTION MANAGEMENT IN ELECTRICITY MARKET USING PARTICLE SWARM OPTIMIZATION WITH TIME VARYING ACCELERATION COEFFICIENTS

2010 ◽  
Author(s):  
Panida Boonyaritdachochai ◽  
Chanwit Boonchuay ◽  
Weerakorn Ongsakul ◽  
Nader Barsoum ◽  
G. W. Weber ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Electricity Market ◽  
Particle Swarm ◽  
Congestion Management ◽  
Time Varying ◽  
Swarm Optimization

Congestion Management Using Hybrid Particle Swarm Optimization Technique

2010 ◽  
Vol 1 (3) ◽  
pp. 51-66 ◽  
Author(s):  
Sujatha Balaraman ◽  
N. Kamaraj
Keyword(s):  
Particle Swarm Optimization ◽  
Electricity Market ◽  
Control Strategies ◽  
Fitness Function ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Congestion Management ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization

This paper proposes the Hybrid Particle Swarm Optimization (HPSO) method for solving congestion management problems in a pool based electricity market. Congestion may occur due to lack of coordination between generation and transmission utilities or as a result of unexpected contingencies. In the proposed method, the control strategies to limit line loading to the security limits are by means of minimum adjustments in generations from the initial market clearing values. Embedding Evolutionary Programming (EP) technique in Particle Swarm Optimization (PSO) algorithm improves the global searching capability of PSO and also prevents the premature convergence in local minima. A number of functional operating constraints, such as branch flow limits and load bus voltage magnitude limits are included as penalties in the fitness function. Numerical results on three test systems namely modified IEEE 14 Bus, IEEE 30 Bus and IEEE 118 Bus systems are presented and the results are compared with PSO and EP approaches in order to demonstrate its performance.

Congestion Management Using Hybrid Particle Swarm Optimization Technique

2012 ◽  
pp. 710-725
Author(s):  
Sujatha Balaraman ◽  
N. Kamaraj
Keyword(s):  
Particle Swarm Optimization ◽  
Electricity Market ◽  
Fitness Function ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Congestion Management ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization ◽  
Bus Voltage

This paper proposes the Hybrid Particle Swarm Optimization (HPSO) method for solving congestion management problems in a pool based electricity market. Congestion may occur due to lack of coordination between generation and transmission utilities or as a result of unexpected contingencies. In the proposed method, the control strategies to limit line loading to the security limits are by means of minimum adjustments in generations from the initial market clearing values. Embedding Evolutionary Programming (EP) technique in Particle Swarm Optimization (PSO) algorithm improves the global searching capability of PSO and also prevents the premature convergence in local minima. A number of functional operating constraints, such as branch flow limits and load bus voltage magnitude limits are included as penalties in the fitness function. Numerical results on three test systems namely modified IEEE 14 Bus, IEEE 30 Bus and IEEE 118 Bus systems are presented and the results are compared with PSO and EP approaches in order to demonstrate its performance.

Congestion Management Using Hybrid Particle Swarm Optimization Technique

2012 ◽  
pp. 165-181
Author(s):  
Sujatha Balaraman ◽  
N. Kamaraj
Keyword(s):  
Particle Swarm Optimization ◽  
Electricity Market ◽  
Fitness Function ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Congestion Management ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization ◽  
Bus Voltage

This paper proposes the Hybrid Particle Swarm Optimization (HPSO) method for solving congestion management problems in a pool based electricity market. Congestion may occur due to lack of coordination between generation and transmission utilities or as a result of unexpected contingencies. In the proposed method, the control strategies to limit line loading to the security limits are by means of minimum adjustments in generations from the initial market clearing values. Embedding Evolutionary Programming (EP) technique in Particle Swarm Optimization (PSO) algorithm improves the global searching capability of PSO and also prevents the premature convergence in local minima. A number of functional operating constraints, such as branch flow limits and load bus voltage magnitude limits are included as penalties in the fitness function. Numerical results on three test systems namely modified IEEE 14 Bus, IEEE 30 Bus and IEEE 118 Bus systems are presented and the results are compared with PSO and EP approaches in order to demonstrate its performance.

Inverse estimation of time-varying heat transfer coefficients for a hollow cylinder by using self-learning particle swarm optimization

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Kun-Yung Chen ◽  
Te-Wen Tu
Keyword(s):  
Heat Transfer ◽  
Particle Swarm Optimization ◽  
Objective Function ◽  
Hollow Cylinder ◽  
General Type ◽  
Particle Swarm ◽  
Time History ◽  
Time Varying ◽  
Swarm Optimization ◽  
Self Learning

Abstract An inverse methodology is proposed to estimate a time-varying heat transfer coefficient (HTC) for a hollow cylinder with time-dependent boundary conditions of different kinds on inner and outer surfaces. The temperatures at both the inner surface and the interior domain are measured for the hollow cylinder, while the time history of HTC of the outer surface will be inversely determined. This work first expressed the unknown function of HTC in a general form with unknown coefficients, and then regarded these unknown coefficients as the estimated parameters which can be randomly searched and found by the self-learning particle swarm optimization (SLPSO) method. The objective function which wants to be minimized was found with the absolute errors between the measured and estimated temperatures at several measurement times. If the objective function converges toward the null, the inverse solution of the estimated HTC will be found eventually. From numerical experiments, when the function of HTC with exponential type is performed, the unknown coefficients of the HTC function can be accurately estimated. On the contrary, when the function of HTC with a general type is conducted, the unknown coefficients of HTC are poorly estimated. However, the estimated coefficients of an HTC function with the general type can be regarded as the equivalent coefficients for the real function of HTC.

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