Indian Journal of Science and Technology

Abstract

This paper reports a multi objective model for Advanced Unit Commitment (AUC) with wind power and Pumped Storage (PS) units using Artificial Fish Swarm Algorithm (AFSA). The novelty of the proposed method is different from the regular operation. A Mutation Operator (MO) is designed to enhance the searching performance of the algorithm and ability to adapt complex optimization problems. Here, theAFSC algorithm to accommodate wind output uncertainty, with the multi-objective of providing an optimal AUC schedule for the thermal generators in the day-ahead market that minimizes the total cost under the different wind power output scenario. The proposed method is more reliable for AUC because it considers the wind power uncertainty using the Auto Regressive Moving Average (ARMA) time-series model and PS units, which significantly reduces the total cost. The feasibility of the proposed method is demonstrated in the MATLAB/simulink platform and tested under IEEE standard 118 bus system and the results are compared with those obtained by Genetic Algorithm (GA), Semi Definite Programming (SDP), Binary Real Coded Firefly Algorithm (BRCFF), Artificial Bee Colony optimization (ABC) in terms of total Operating cost. The simulation result shows that the proposed method is capable of solving higher quality solutions.

Keywords: Artificial Fish Swarm Algorithm (AFSA), Auto Regressive Moving Average (ARMA), Advanced Unit Commitment (AUC), Pumped Storage (PS), Mutation Operator (MO)