Computational Intelligence Based Technique for Fuel Cost Minimization in Small and Bulk Power

Abstract

Fuel is one of the important sources in the electricity generation. However, due the fluctuation of the crude oil; the cost of generation of electricity will be much affected. Thus, a pre-offline study could be one of the acceptable efforts for the power system planner to conduct such measure in the avoidance of undesired event. This will require an optimization process to ensure the optimal parameters are identified to achieve their pre-determined objective. This paper presents the application of evolutionary programming (EP) algorithm for fuel cost minimization. The EP technique has been tested on IEEE30-Reliability Test System (RTS) and IEEE 118-Reliability Test System (RTS) under several scenarios. The simulated scenarios are (i) base case, (ii) stressed condition (iii) line outage condition and (iv) generator outage condition. With the forecasted four scenarios, a power system operator or planner will have initial information of the system status during the offline studies. Results obtained from the study would be beneficial to the system utility for any remedial action for power operation. © 2020, World Academy of Research in Science and Engineering. All rights reserved.