Abstract:
This paper deals with the cost optimization of a hybrid solar, wind and hydropower plant using a Particle Swarm
Optimization (PSO) approach. PSO is a technique that belongs to Swarm intelligence, an artificial intelligence (AI) technique,
known as a Meta-heuristic optimization solver, mostly used in Biology. With the consideration of solar, wind and hydro hybrid
system which has become extremely relevant for developing countries, and also the existence of a wide list of constraints, the
adoption of PSO technique cannot be avoided. On the other hand, a linear optimization approach was used with Matlab software
to solve the same problem. Both techniques were applied to secondary data collected from RetScreen Plus software for the
location Accra and results were extracted in terms of distribution of supply by individual sources and cost of hybrid system
electricity. Results show in general, an improvement of hybrid system cost of electricity. A histogram was used to show the
distribution of supply for the particular load and the equivalent cost of hybrid system that corresponds to it. A khi-sqaure test
was run to compare the two series of data obtained from the two approaches adopted. The Khi-square test show high similarity
confirming the reliability of the PSO approach which on the other hand presents the advantage of scalability over a wider range
of sources with multiple constraints.
