Basking in the sun

Multiobjective optimization of active solar energy systems using Optimus and Scilab

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With Optimus and Scilab combined, engineers optimized the performance and cost of an active solar energy system that uses pumps to circulate fluid through the solar collectors.

Scilab scripts easily integrated into Optimus workflow

Using straightforward XML-files respecting a very simple syntax, a Scilab User Customizable Action (UCA) can be written in a very short time to integrate Scilab into any Optimus workflow. This frees engineers from repetitive administrative tasks and delivers a repeatable automated simulation process

Multiobjective design optimization performed in seconds

Optimus multiobjective Particle Swarm Optimization delivered an accurate Pareto front, allowing for identifying an optimized configuration that boosts solar energy system efficiency at a low overall cost. Using surrogate models for optimization is much more efficient - allowing to perform the optimization in seconds.

Taking into account daily changing climate conditions to ensure robust performance

A Monte Carlo analysis was performed, introducing a stochastic daily clearness index into the analysis rather than using the average year-round clearness index as a fixed parameter. The analysis was done to take into account small perturbations on the clearness index, and reported a probability of failure of only 0.4% in delivering a minimally required system performance.


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