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Multiobjective optimization of a solar energy system through the combined use of
Optimus & Scilab [email protected], Application Engineer
ScilabTEC, 6th International Scilab Users Conference, 15th-16th May 2014, Paris
Outline
• Optimus overview
• Integrating Scilab in Optimus
• Multiobjective optimization of a active solar energy system
• Future Development
• Conclusions, Q&A
Optimus: A collection of technologies
Creating a repeatable, automated process: Multi code, Multi CPU, Synced, Logic control, Repeatable
Rapid identification of key design variables: Histograms, Sobol indices, Correlations, ANOVA, Taguchi
Process Integration Design of Experiments
Use experiments to create best fit continuous surface (“surrogate modeling”), results of any set of inputs parameters available in under a second, portable mathematical model
Response surface models Single and multi-objective optimization. Minimize sensitivity for robust design optimization, reduce global product failure probability with Six-Sigma quality. Ensure reliability of the product: Design For Six Sigma (DFSS)
Optimization & Robust Design
Noesis Solutions
… more than 15+ years & 100+ person-years experience in Simulation Process Automation & Design Optimization. The largest OEM provider of embedded optimization.
… sales offices across Europe, US and Asia realizing double-digit profit growth for 15+ years.
A leading software & services provider
A strong worldwide presence
Leading Solutions for Engineering Optimization
Noesis Solutions Most extensively deployed software of its kind…
Integrating Scilab in Optimus
1
Integrating Scilab scripts in Otimus
• The easiest way to integrate Scilab is by means of a “User Customizable Action” (UCA).
• A UCA can be used for any software simulation tool that can be run in batch mode. The UCA is easily configured with XML-files, respecting a very simple syntax
• Optimus provides an easy-to-use graphical drag and drop interface to quickly create a multidisciplinary workflow that contains Scilab scripts
Scilab UCA
The workflow is changing each time the values of a vector and it is finding the best configuration that minimizes the difference between the resulting curve and a target curve
Multiobjective optimization of a
active solar energy system
2
Optimizing an active solar energy system
To demonstrate the benefit of having an independent platform integrating different software combined with Scilab, we present an example that optimizes the performance and costs of a solar energy system.
Optimus
Scila
b
Active Solar Energy Sytems Toolbox
• The toolbox implements the φ-f chart method with Scilab
• It computes the solar fraction for hot water production with respect to the global request
• The work is licensed by Openeering under a Creative Commons (Attribution-
NonCommercial-NoDerivs) (courtesy of Openeering)
Scilab toolbox
Parametric usage of the toolbox
Input Min Max Default Unit
Collector Area 1 20 4 [m2]
Storage Capacity 100 1000 120 [l]
Tilt Angle 0 90 45 [deg]
Responses Description Goals
mean(F_tl) Average load supply by solar energy Maximize
Cost Total cost of the solar energy system Minimize
• The toolbox is valid for collector azimuth angles of 180 degrees, i.e. the collector faces the south
• Data for Rome
Process Integration
Running the Scilab script
Computing the global cost of the system (Excel)
Automate: Running the toolbox & cost analysis
Multiobjective Optimization
• To deal with multiple objectives, discrete variables and non-linear responses a multiobjective particle swarm optimization (PSO) algorithms is used.
• The PSO algorithm supports parallel execution of experiments and delivers a highly accurate Pareto front.
Pareto Points
Post-processing
• Models for cost and efficiency
• Feasible and unfeasible designs
• Parallel coordinates for filtering best options
• Clustering methods….
Selecting a good point
• This point represent a good trade-off between costs and efficiency
• Big increase in efficiency with a very small increase in term of costs
• Is this point robust?
Pareto Points
Probabilistic optimization
• The amount of solar radiation that reaches the ground depends on the climatic conditions (e.g. cloud cover).
• Cloudiness is the main factor affecting the difference between the values of solar radiation measured outside the atmosphere and on earthly surface.
• A daily clearness index is defined as the ratio of daily solar radiation to the extraterrestrial daily solar radiation
• This variable should be consider as a stochastic variable
Running a Monte Carlo Analysis
• The average clearness index for all the months is substituted by a stochastic distribution
• Several configurations are automatically run making small perturbations on the clearness index
Comparing robustness
• [left] Very robust solutions without failure (only one design and more than needed)
• [right] High probability of failure (17.4%)
Future Development 3
UCI
• The integration of Scilab can be even extended by means of what is called a “User Customizable Interface (UCI)”
• Thanks to a wrapping layer, based on a similar XML technique, it is possible to directly access all Scilab parameters and results.
• The data exchange between Optimus and Scilab is then more direct and the user doesn’t need to read and write external ASCII files.
Integrating the algorithms
• Optimization algorithms
• Response surface methods (interpolation)
General Conclusions
• Easy integration of Scilab Scripts in Optimus
• Multidisciplinary problems involving Scilab or Xcos
• Trade-off analysis: Cost vs. Efficiency
• Avoid product failure by choosing a robust and reliable optimum
With Scilab and Optimus you can
Benefits
Save Time Consolidate Knowledge
• Drive Scilab and combine with other tools
• Automate Repetitive Tasks
• Maximize efficient use of your simulation resources
• Simplify your design work by focusing on key parameters
• Automate parametric studies
• Intelligent optimization methods
• Create fast and accurate meta models
• Share model data through Excel, etc…
Improve Performance
