MATEC Web Conf.
Volume 136, 20172017 2nd International Conference on Design, Mechanical and Material Engineering (D2ME 2017)
|Number of page(s)||7|
|Section||Chapter 2: Design|
|Published online||14 November 2017|
Optimization of Mangala Hydropower Station, Pakistan, using Optimization Techniques
1 Research center of Fluid Machinery Engineering & Technology, Jiangsu University, P.R. China.
2 Department of Irrigation and Drainage, University of Agriculture, Faisalabad, Pakistan.
3 Center of Excellence in Water Resources, University of Engineering & Technology, Lahore, Pakistan.
4 School of Agricultural Equipment Engineering, Jiangsu University, P.R. China.
5 Department of Structures & Environmental Engineering, University of Agriculture, Faisalabad, Pakistan.
6 School of Environment and safety Engineering, Jiangsu University, P.R. China.
a Corresponding author: firstname.lastname@example.org
Hydropower generation is one of the key element in the economy of a country. The present study focusses on the optimal electricity generation from the Mangla reservoir in Pakistan. A mathematical model has been developed for the Mangla hydropower station and particle swarm and genetic algorithm optimization techniques were applied at this model for optimal electricity generation. Results revealed that electricity production increases with the application of optimization techniques at the proposed mathematical model. Genetic Algorithm can produce maximum electricity than Particle swarm optimization but the time of execution of particle swarm optimization is much lesser than the Genetic algorithm. Mangla hydropower station can produce up to 59*109 kWh electricity by using the flows optimally than 47*108 kWh production from traditional methods.
Key words: Mangla Reservoir / Hydropower / Optimization techniques / PSO
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.