Title: Bidding strategy in south region day-ahead demand model by improved artificial bee colony algorithm
Authors: Velamuri Madhu Sudana Reddy; Badathala Subramanyam; Munagala Suraya Kalavathi
Addresses: Visvodaya Engineering College, Kavali, India ' Faculty of Engineering in S.V.U.C.E., Tirupathi, India ' J.N.T.U.H, Hyderabad, India
Abstract: In this paper, an improved artificial bee colony (ABC) technique-based modelling of the optimal bidding strategies for a competitive electricity market is proposed. The novelty of the proposed method is improved searching ability by utilising the bat-inspired algorithm in the place of scout bee phase. In the proposed method optimum bidding parameters are determined by using the two phases of the ABC. From the optimised parameters the exact solution is predicted by using Bat-inspired algorithm. Here, the bee's population velocity and the position vector are improved to find the exact bidding parameters. The Indian Energy Exchange (IEX) hourly-based load demand dataset is utilised for the learning and testing the artificial neural network (ANN). This action makes the ABC as an improved technique. Finally, the proposed method is implemented in the MATLAB/Simulink platform and effectiveness is analysed by using the comparison of different techniques like ABC algorithm, particle swarm optimisation (PSO) algorithm, ABC-PSO and ABC-Cuckoo Search (CS). The comparison results demonstrated the superiority of the proposed approach and confirm its potential to solve the problem.
Keywords: artificial bee colony; ABC; bat algorithm; artificial neural networks; ANNs; optimal bidding; electricity markets; bidding strategy; day-ahead demand modelling; metaheuristics; India; simulation.
International Journal of Power and Energy Conversion, 2017 Vol.8 No.2, pp.204 - 224
Received: 06 Dec 2014
Accepted: 26 Aug 2015
Published online: 22 Mar 2017 *