The aim and objective of this research is to formulate and solve a multi-objective optimization problem
for the optimal placement of multiple rows and multiple columns of fixed flat-plate solar collectors
in a field. This is to maximize energy collected from the solar collectors and minimize the
investment in terms of the field and collector cost. The resulting multi-objective optimization problem
will be solved using genetic algorithm techniques.
It is necessary to consider multiple columns of collectors as this can result in obtaining higher amounts
of energy from these collectors when costs and maintenance or replacement of damaged parts are
concerned. The formulation of such a problem is dependent on several factors, which include shading
of collectors, inclination of collectors, distance between the collectors, latitude of location and the
global solar radiation (direct beam and diffuse components). This leads to a multi-objective optimization
problem. These kind of problems arise often in nature and can be difficult to solve. However
the use of evolutionary algorithm techniques has proven effective in solving these kind of problems.
Optimizing the distance between the collector rows, the distance between the collector columns and
the collector inclination angle, can increase the amount of energy collected from a field of solar collectors
thereby maximizing profit and improving return on investment.
In this research, the multi-objective optimization problem is solved using two optimization approaches
based on genetic algorithms. The first approach is the weighted sum approach where the
multi-objective problem is simplified into a single objective optimization problem while the second
approach is finding the Pareto front.
Dissertation (MEng)--University of Pretoria, 2013.