International Journal of Applied Science and Technology

ISSN 2221-0997 (Print), 2221-1004 (Online)

A Fast Algorithm for Reactive Power Market Management Using Artificial Neural Networks
A. Kargarian, R. Sarkari Khorrami


This paper presents a novel algorithm to find optimal reactive power market schedule in deregulated electricity markets. There are many factors which can impact on optimal system operating point. Furthermore, the number of control and decision variables in an optimization problem in a general power system is very large. Therefore, finding the optimal reactive power market schedule may be very time consuming. In order to overcome this deficiency, the proposed algorithm suggests using Artificial Neural Networks (AANs) to speed up the market clearing process. In this algorithm, at first, many different samples should be produced by traditional market clearing process. Then, using these samples an ANN will be trained to fined optimal reactive power market schedule. The proposed algorithm is tested on IEEE 24-bus test system with satisfactory results.


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