A Messy Genetic Algorithm Based Optimization Scheme for SVC Placement of Power Systems under Critical Operation Contingence

2008 ◽  
Author(s):  
J.S. Huang ◽  
Michael Negnevitsky
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Optimization Scheme

scholarly journals Demand Bidding Optimization for an Aggregator with a Genetic Algorithm

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2498 ◽  
Author(s):  
Leehter Yao ◽  
Wei Lim ◽  
Sew Tiang ◽  
Teng Tan ◽  
Chin Wong ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Optimization Scheme ◽  
Demand And Supply ◽  
Purchase Cost ◽  
Highly Nonlinear ◽  
Bidding Price ◽  
Load Curtailment ◽  
Hourly Basis ◽  
Bidding Models

Demand response (DR) is an effective solution used to maintain the reliability of power systems. Although numerous demand bidding models were designed to balance the demand and supply of electricity, these works focused on optimizing the DR supply curve of aggregator and the associated clearing prices. Limited researches were done to investigate the interaction between each aggregator and its customers to ensure the delivery of promised load curtailments. In this paper, a closed demand bidding model is envisioned to bridge the aforementioned gap by facilitating the internal DR trading between the aggregator and its large contract customers. The customers can submit their own bid as a pairs of bidding price and quantity of load curtailment in hourly basis when demand bidding is needed. A purchase optimization scheme is then designed to minimize the total bidding purchase cost. Given the presence of various load curtailment constraints, the demand bidding model considered is highly nonlinear. A modified genetic algorithm incorporated with efficient encoding scheme and adaptive bid declination strategy is therefore proposed to solve this problem effectively. Extensive simulation shows that the proposed purchase optimization scheme can minimize the total cost of demand bidding and it is computationally feasible for real applications.

Genetic algorithm-based method for contingency screening in power systems

1996 ◽  
Vol 116 (2) ◽  
pp. 99-111 ◽  
Author(s):  
Hiroumi Saitoh ◽  
Yutaka Takano ◽  
Junichi Toyoda
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Contingency Screening

scholarly journals Multiobjective Optimization Methods for Congestion Management in Deregulated Power Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
K. Vijayakumar
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Electricity Market ◽  
Optimization Methods ◽  
Evolutionary Programming ◽  
Congestion Management ◽  
Transmission Congestion ◽  
Nsga Ii ◽  
Line Overload ◽  
Nondominated Sorting

Congestion management is one of the important functions performed by system operator in deregulated electricity market to ensure secure operation of transmission system. This paper proposes two effective methods for transmission congestion alleviation in deregulated power system. Congestion or overload in transmission networks is alleviated by rescheduling of generators and/or load shedding. The two objectives conflicting in nature (1) transmission line over load and (2) congestion cost are optimized in this paper. The multiobjective fuzzy evolutionary programming (FEP) and nondominated sorting genetic algorithm II methods are used to solve this problem. FEP uses the combined advantages of fuzzy and evolutionary programming (EP) techniques and gives better unique solution satisfying both objectives, whereas nondominated sorting genetic algorithm (NSGA) II gives a set of Pareto-optimal solutions. The methods propose an efficient and reliable algorithm for line overload alleviation due to critical line outages in a deregulated power markets. The quality and usefulness of the algorithm is tested on IEEE 30 bus system.

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