scholarly journals Congestion Management under Hybrid Electricity Market using Self-organizing Hierarchical Particle Swarm Optimization

2013 ◽  
Vol 82 (17) ◽  
pp. 39-45 ◽  
Author(s):  
Manasarani Mandala ◽  
C. P. Gupta
Keyword(s):  
Particle Swarm Optimization ◽  
Electricity Market ◽  
Particle Swarm ◽  
Congestion Management ◽  
Swarm Optimization ◽  
Self Organizing

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.

scholarly journals Models for Short-Term Wind Power Forecasting Based on Improved Artificial Neural Network Using Particle Swarm Optimization and Genetic Algorithms

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2873 ◽  
Author(s):  
Dinh Thanh Viet ◽  
Vo Van Phuong ◽  
Minh Quan Duong ◽  
Quoc Tuan Tran
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Wind Power ◽  
Optimization Algorithm ◽  
Electricity Market ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Short Term ◽  
Swarm Optimization

As sources of conventional energy are alarmingly being depleted, leveraging renewable energy sources, especially wind power, has been increasingly important in the electricity market to meet growing global demands for energy. However, the uncertainty in weather factors can cause large errors in wind power forecasts, raising the cost of power reservation in the power system and significantly impacting ancillary services in the electricity market. In pursuance of a higher accuracy level in wind power forecasting, this paper proposes a double-optimization approach to developing a tool for forecasting wind power generation output in the short term, using two novel models that combine an artificial neural network with the particle swarm optimization algorithm and genetic algorithm. In these models, a first particle swarm optimization algorithm is used to adjust the neural network parameters to improve accuracy. Next, the genetic algorithm or another particle swarm optimization is applied to adjust the parameters of the first particle swarm optimization algorithm to enhance the accuracy of the forecasting results. The models were tested with actual data collected from the Tuy Phong wind power plant in Binh Thuan Province, Vietnam. The testing showed improved accuracy and that this model can be widely implemented at other wind farms.

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