NDLI: Stability constrained optimal power flow for the balancing market using genetic algorithms

Content Provider	IEEE Xplore Digital Library
Author	Zhang, X. Dunn, R.W. Li, F.
Copyright Year	2003
Description	Author affiliation: Sch. of Electron. & Electr. Eng., Univ. of Bath, UK (Zhang, X.; Dunn, R.W.; Li, F.)
Abstract	Angle stability (both transient and oscillatory) is an important constraint in power system operation. The work presented in this paper describes a genetic algorithm (GA) based approach for solving the problem of angle stability constrained optimal power flow. The control parameter modeled in the chromosome of the GA is generation power of the units. The application presented here is the UK balancing market using balancing mechanism units (BMUs). The BMUs put into the GA list depend on either their bid/offer price or their impact of generation change on system stability. Sensitivity factors, obtained by doing perturbations, are used to represent a BMU's impact on system stability. A novel mapping method is employed to maintain power balance. Stability constraints are dealt with as penalty cost, and their contribution to the fitness of the objective function is evaluated independently, so that the search for the optimal solution concentrates on feasible solutions. Tests on a reduced UK system show that the proposed GA is able to cope with the highly nonlinear optimization problem. Numerical simulation results of the test system are presented.
Starting Page	932
Ending Page	937
File Size	495623
Page Count	6
File Format	PDF
ISBN	0780379896
DOI	10.1109/PES.2003.1270433
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-07-13
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Load flow Genetic algorithms Power system stability Power system transients Power system modeling System testing Biological cells Power generation Cost function Numerical simulation
Content Type	Text
Resource Type	Article

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