Global Advanced Research Journal of Engineering, Technology and Innovation (GARJETI) SSN: 2315-5124 January 2012 Vol. 2(1), pp 011-019

Copyright © 2013 Global Advanced Research Journals   

 

Original Research Articles

Electricity Distribution Scheduling For Gaza Strip Using Genetic Algorithm 

Mohammed T. Hussein1 , Wazen M. Shbair2, Lina  J. Mghari3 ,Samera M. Bader

1Electrical Eng. Dept., IUG, Palestine

2Computer Eng. Dept., IUG, Palestine

3Computer Eng. Dept., IUG, Palestine

4Computer Eng. Dept., IUG, Palestine 

Corresponding author Email: wmshbair@iugaza.edu.ps 

Accepted 07 January 2012

 

Abstract 

This paper presents a genetic algorithm based approach to the scheduling of electricity distribution. Gaza strip challenges shortage of electricity power. Electricity Distribution Company overcomes this shortage by dividing the cities into sub-regions and providing them with electricity power in alternating manner. But this approach suffer from a lot of problems for regions contains hospital and sensitive facility, also it is the lack of systematic approach to schedule the distribution of electricity. The proposed algorithm provides a schedule for turn on/off transformers in sub-regions in order to minimize the number of turn-off transformers. For example, if the total power consumption for a group of transformers is 15 m.W, but the available power is 9 m.W, the genetic algorithm provides a schedule to distribute 9 m.W on transformed with minimum number of transformers turn off. Also the algorithm can be adapted according to customer power consumption in different seasons. All constrains provided by the electricity distribution company are considered in the genetic algorithm design. The main advantage of the genetic algorithm formulation is that fairly accurate results can be obtained. The algorithm has been tested on a power line contains 85 transformers and the number of turn off transformer reduced to 30%, which will reduce the duration of electricity discontinuity and provide better service to customers. 

Keywords: Electric-Power Scheduling, Genetic algorithms, Scheduling.

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