Advanced hybrid genetic algorithm for short-term generation scheduling


El Desouky, A. A., Aggarwal, R., Elkateb, M. M. and Li, F., 2001. Advanced hybrid genetic algorithm for short-term generation scheduling. Generation, Transmission and Distribution, IEE Proceedings-, 148 (6), pp. 511-517.

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This paper is concerned with the development of hybrid techniques for short-term generation scheduling in which the basic element is a genetic algorithm (GA). Particular emphasis is placed on investigating hybrid approaches, involving combining a GA with an artificial neural network (ANN) and/or a priority list (PL), which are significantly more cost effective than the techniques available hitherto. First, a prescheduling of the generating units using the ANN is presented. Since the solution attained via the ANN may not be feasible for the entire scheduling period, the GA (or the GA with a PL) is subsequently employed to adjust the prescheduling. To take load-forecast uncertainty into account and to make the schedule feasible and cost effective, a typical ±5% load forecast error is incorporated into the ANN training data. The proposed techniques are shown to be efficient and flexible based on the simulations performed on the IEEE reliability test systems. In addition, the proposed hybrid techniques are shown to have higher potential to enhance performance and expedite the solution, overcoming the problems associated with the traditional GA-based technique


Item Type Articles
CreatorsEl Desouky, A. A., Aggarwal, R., Elkateb, M. M. and Li, F.
Uncontrolled Keywordslearning (artificial intelligence),genetic algorithms,ieee reliability test systems,ann,ann training data,hybrid genetic algorithm,load forecasting,power generation scheduling,generation scheduling short-term,artificial neural network,load forecast error,load-forecast uncertainty,power system analysis computing,priority list,generating units prescheduling
DepartmentsFaculty of Engineering & Design > Electronic & Electrical Engineering
ID Code6164


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