@inproceedings{1890d02848444b9fbe6343dc5c7f0d2d,
title = "Artificial intelligence based optimization algorithm for thermal power generation scheduling incorporating demand response strategy",
abstract = "A dynamic combined economic emission dispatch (CEED) problem incorporating demand response strategy is performed. The demand response optimisation problem is solved using a nonconvex mixed binary integer programming technique. Fixed and flexible loads connected to the power system network are considered in the analysis. Optimisation of the dynamic CEED problem is done using particle swarm optimisation (PSO) technique. The algorithm developed is able to take into account the thermal power generation unit ramp rates and power generation constraints. Conventional Lambda iterative method is used to validate the proposed PSO algorithm. The results show that the proposed PSO algorithm performs better than the conventional Lambda iterative method.",
author = "Oliver Dzobo and Yanxia Sun",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference, PRASA-RobMech 2016 ; Conference date: 30-11-2016 Through 02-12-2016",
year = "2017",
month = jan,
day = "10",
doi = "10.1109/RoboMech.2016.7813134",
language = "English",
series = "2016 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference, PRASA-RobMech 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2016 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference, PRASA-RobMech 2016",
address = "United States",
}