A computational method for optimal design of the multi-tower heliostat field considering heliostats interactions

In multi-tower heliostat fields, although heliostats are capable of aiming at different receivers during the day, due to different orientations, neighboring heliostats might affect shading and blocking efficiency of each other reciprocally. In the proposed method of this paper, considering the mentioned effects and based on a group decision-making approach, each heliostat chooses the best receiver thus ensuring the highest possible instantaneous efficiency of the field. As a case study, this method is applied for the optimal design of a multi-tower field. Then, the field performance is simulated in a case where heliostats make decisions individually without considering the interactions. Finally, these results are compared with separated single tower fields' energy performance. Results of the case study show that, due to the high dependency on shading and blocking factor, the annual efficiency of the multi-tower field without considering the interactions is only slightly higher than the two separated single tower fields. However, using the proposed method, the optical performance of the multi-tower field improves and the annual efficiency of 54.58% is reachable, which is 0.21% higher than the case without considering the interactions and 0.26% higher than the separated single tower fields.

[1]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[2]  Amos Danielli,et al.  Improving the optical efficiency of a concentrated solar power field using a concatenated micro-tower configuration , 2011 .

[3]  Detlef P. van Vuuren,et al.  Assessing current and future techno-economic potential of concentrated solar power and photovoltaic electricity generation , 2015 .

[4]  G. Sassi.  Some notes on shadow and blockage effects , 1983 .

[5]  Ajith Abraham,et al.  Inertia Weight strategies in Particle Swarm Optimization , 2011, 2011 Third World Congress on Nature and Biologically Inspired Computing.

[6]  B L Kistler,et al.  A user's manual for DELSOL3: A computer code for calculating the optical performance and optimal system design for solar thermal central receiver plants , 1986 .

[7]  Abdallah Khellaf,et al.  A review of studies on central receiver solar thermal power plants , 2013 .

[8]  Germain Augsburger,et al.  From Single- to Multi-Tower Solar Thermal Power Plants: Investigation of the Thermo-Economic Optimum Transition Size , 2012 .

[9]  Li Li,et al.  A New Boundary Condition for Particle Swarm Optimization , 2010, J. Convergence Inf. Technol..

[10]  David R. Mills,et al.  Multi-tower solar array , 2003 .

[11]  Francisco J. Collado,et al.  A review of optimized design layouts for solar power tower plants with campo code , 2013 .

[12]  Robert Pitz-Paal,et al.  Visual HFLCAL - A Software Tool for Layout and Optimisation of Heliostat Fields , 2009 .

[13]  Pierre Garcia,et al.  Codes for solar flux calculation dedicated to central receiver system applications : A comparative review , 2008 .

[14]  Manuel Torrilhon,et al.  Heliostat field optimization: A new computationally efficient model and biomimetic layout , 2012 .

[15]  Viorel Badescu,et al.  Modeling Solar Radiation at the Earth's Surface: Recent Advances , 2014 .

[16]  F.M.F Siala,et al.  Mathematical formulation of a graphical method for a no-blocking heliostat field layout , 2001 .

[17]  D. Yogi Goswami,et al.  A computationally efficient method for the design of the heliostat field for solar power tower plant , 2014 .

[18]  Francisco J. Collado,et al.  Campo: Generation of regular heliostat fields , 2012 .

[19]  Mustafa Eltaher Elayeb,et al.  Calculation of the Blocking Factor in Heliostat Fields , 2014 .

[20]  S. Kalogirou.  Solar Energy Engineering: Processes and Systems , 2009 .