Genetic algorithm optimization of heliostat field layout for the design of a central receiver solar thermal power plant
Muhammad Haris,
Atiq Ur Rehman,
Sheeraz Iqbal,
Syed Owais Athar,
Hossam Kotb,
Kareem M. AboRas,
Abdulaziz Alkuhayli,
Yazeed Yasin Ghadi,
Kitmo
Affiliations
Muhammad Haris
Department of Electrical Engineering, Balochistan University of Information Technology, Engineering and Management Sciences (BUITEMS), Quetta 87300, Pakistan
Atiq Ur Rehman
Department of Electrical Engineering, Balochistan University of Information Technology, Engineering and Management Sciences (BUITEMS), Quetta 87300, Pakistan
Sheeraz Iqbal
Department of Electrical Engineering, University of Azad Jammu and Kashmir, Muzaffarabad, Pakistan
Syed Owais Athar
Department of Electronic Engineering, Balochistan University of Information Technology, Engineering and Management Sciences (BUITEMS), Quetta 87300, Pakistan
Hossam Kotb
Department of Electrical Power and Machines, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt
Kareem M. AboRas
Department of Electrical Power and Machines, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt
Abdulaziz Alkuhayli
Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Yazeed Yasin Ghadi
Department of Computer Science and Software Engineering, Al Ain University, Abu Dhabi 15322, United Arab Emirates
Kitmo
University of Maroua, National Advanced School of Engineering of Maroua, Department of Renewable Energy, P.O. Box 46 Maroua, Cameroon; Corresponding author.
The heliostat field layout in a central receiver solar thermal power plant has significant optical losses that can ultimately affect the overall output power of the plant. In this paper, an optimized heliostat field layout based on annual efficiency and power of 50 MW for the local coordinates of Quetta, Pakistan, is proposed. The performance of two different heliostat field layouts such as radial staggered and Fermat's spiral distribution are evaluated and different design points in a year are considered for the analysis. The field layouts are then optimized using a rejection sampling based Genetic Algorithm (GA). It considers the output power and mean overall efficiency for vernal equinox, summer solstice, autumnal equinox, and winter solstice as objective functions. The GA optimizes the heliostat field parameters, namely, security distance (DS), tower height (TH), heliostat width to length ratio (WR), and the length of heliostats (LH). The study system was developed in MATLAB for validation. It was observed that for the radial staggered layout, the number of heliostats decreased by 364 and the efficiency was improved by 8.52 % using GA optimization relative to unoptimized results field layout. The annual efficiency for Fermat's spiral configuration was improved by 14.62 % and correspondingly, the number of heliostats decreased by 434.
