Download Improving Efficiency of Evolutionary Algorithms - RWTH

Improving Efficiency of Evolutionary Algorithms
used for
Renewable Energy Power Plant Layouts
Lukas Netz
Abstract
Algorithm
Different configurations of evolutionary algorithms are used to optimize the layout of both a
solar power plant and an offshore wind park. Four different encodings are used to develop
an improved configuration of the algorithm to provide energy efficient layouts in minimal
runtime. The results are validated by comparison with rule based optimizers. This work is
based on [4] and [3] and ment to evalute the different effects of Evolutionary Algorithms and
Genetic Algorithms on both models.
Model
The efficiency of both solar power stations and offshore windparks depends highly on the
placement of the individual energy providing modules. In both cases a general rule for optimal
placement is not yet known. Local constraints like restricted areas and surface conditions
influence the placement.
Fig. 3: Basic process of an evolutionary algorithm [2]
Fig. 1: Offshore Windparks Horns Rev 2 and Horns Rev 3
Fig. 2: Solar tower power plants PS10 and PS20
Discrete Placement
Continous Placement
Fig. 4: GA Genome encoding for 3D parameters
Fig. 5: EA Genome encoding for 2D parameters
Boolean encoding is used to determine whether a heliostat (or wind turbine) will be placed at
the corresponding position. There are n values in a chromosome mapped to the n positions on
the power plant area. The precision of the model depends on the resolution of the mapping.
A set of n tuples is used to encode the positions of heliostats (or wind turbines). Both x
and y position can be sampled without the need of discretization.
Convergence of Evolutionary Algorithms
Next Steps
The ulterior motive of these experiments is to find a optimal discretization that has a low
runtime while still providing results with a high quality.
1.00
0.98
• Evaluate differences in convergence behavior between discrete and continuous model.
• Evaluate the influence of the discretization on both runtime and quality of results.
Fitness
0.96
• Evaluate reachability of results in context of discretization.
• Compare performance gain of discretization with performance of rule based models.
0.94
0.92
Collaboration
Population Size = 1000
Population Size = 200
Population Size = 50
0.90
0.88
0
20
40
Generations
60
80
Joint work with Erika Ábrahám
Theory of Hybrid System, RWTH Aachen
100
Fig. 6: Influence of population size on convergence [1]
The convergence behavior of an evolutionary algorithm can easily be influenced. Increasing
the population size improves the chance to reach better results, but also increases runtime.
References
[1] S. Geulen and M. Josevski and J. Nellen and J. Fuchs and L. Netz and B. Wolters and D. Abel and E. Ábrahám and W. Unger
Learning-based control strategies for hybrid electric vehicles 2015 IEEE Conference on Control Applications (CCA))
[2] Wolters and L. Netz and J. Nellen Open Source C++ Genetic Algorithm Library http://www.geneial.org/ B.
[3] G. Mosetti, C. Poloni, and B. Diviacco, Optimization of wind turbine positioning in large wind farms by means of a genetic
algorithm, Journal of Wind Engineering and Industrial Aerodynamics, 51 (1994), pp. 105–116.
[4] P. Richter, M. Frank and E.Abraham Multi-objective optimization of solar tower heliostat fields. In Proceedings of the 18th
European Conference on Mathematics for Industry (ECMI’14), volume 22 of Mathematics in Industry. Springer, 2014.
LATEX TikZposter