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A Detailed Comparison of Meta- Heuristic Methods for Optimising Wave Energy Converter Placements Mehdi Neshat, Bradley Alexander, Markus Wagner, Yuanzhong Xia GECCO 18 Optimisation and Logistics Group Slide 1 Growth of Renewable Energy

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SLIDE 1

A Detailed Comparison of Meta- Heuristic Methods for Optimising Wave Energy Converter Placements

Mehdi Neshat, Bradley Alexander, Markus Wagner, Yuanzhong Xia

GECCO ‘18 Optimisation and Logistics Group Slide 1

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SLIDE 2

Growth of Renewable Energy

  • Renewable energy – (wind and solar) are now the

cheapest form of new-build power generation. – Solar contracts ~US 2c/kWh

  • (Saudi Arabia – 1.79c kWh (the national Abu Dhabi – Jan 2018)).
  • Growing level of investment

– Global investment US $263 billion in 2016

  • (source IRENA, Jan 2018)

GECCO ‘18 Optimisation and Logistics Group Slide 2

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SLIDE 3

Problem

  • Plenty of renewables…

– South Australia

  • 15th July, 2018

GECCO ‘18 Optimisation and Logistics Group Slide 3 Source: NEM-Watch

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SLIDE 4

Problem

  • Plenty of renewables…

– South Australia

  • 15th July, 2018

GECCO ‘18 Optimisation and Logistics Group Slide 4

Demand ~1.8GW

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SLIDE 5

Problem

  • Plenty of renewables…

– South Australia

  • Morning 15th July, 2018

GECCO ‘18 Optimisation and Logistics Group Slide 5

Renewable’s Share >80%

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SLIDE 6

Problem

  • ..but intermittent.

GECCO ‘18 Optimisation and Logistics Group Slide 6 Source: Open-NEM

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SLIDE 7

Problem

  • ..but intermittent.

GECCO ‘18 Optimisation and Logistics Group Slide 7

15th July

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SLIDE 8

Problem

  • ..but intermittent.

GECCO ‘18 Optimisation and Logistics Group Slide 8

But what about here!

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SLIDE 9

Problem

  • current role of storage

GECCO ‘18 Optimisation and Logistics Group Slide 9

Note the battery (world’s largest)

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SLIDE 10

Possible Solutions

  • Need to smooth and/or time-shift generation
  • Alternatives

– More-connected Grid?

  • Helps but expensive

– Pumped Hydropower?

  • Need water and hills

– Batteries?

  • Fast and efficient but still too small..

– Gas Peaking?

  • Can be expensive + carbon emissions.

GECCO ‘18 Optimisation and Logistics Group Slide 10

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SLIDE 11

Wave Energy

  • Several potential advantages
  • Low correlations with local wind

– Correlated with distant winds

  • High Capacity Factor

– Over 70%

  • High Energy Density

– Over 60 times that of solar per m2

GECCO ‘18 Neshat, et. al. Optimisation and Logistics Group Slide 11

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SLIDE 12

Wave Energy Converters

GECCO ‘18 Optimisation and Logistics Group Slide 13

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SLIDE 13

Wave Energy Converters

GECCO ‘18 Optimisation and Logistics Group Slide 14

buoys

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SLIDE 14

Wave Energy Converters

GECCO ‘18 Optimisation and Logistics Group Slide 15

buoys

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SLIDE 15

Wave Energy Converters

GECCO ‘18 Optimisation and Logistics Group Slide 16

buoys

Problem: Place buoys to maximise p

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SLIDE 16

Wave Energy Converters

GECCO ‘18 Optimisation and Logistics Group Slide 17

buoys

Problem: Place buoys to maximise p

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SLIDE 17

Buoy Placement is Non-trivial

  • Question: Why not just place buoys all in a line, as far

apart as possible?

  • Constructive Interference – You can get more energy
  • fftake by placing buoys close to each other.

– But not too close! – Depends on local wave conditions.

  • And, interactions become complex as more buoys

are added.

  • And, the size of wave farms is limited.

GECCO ‘18 Optimisation and Logistics Group Slide 18

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SLIDE 18

Other Work

GECCO ‘18 Optimisation and Logistics Group Slide 19

Authors (year) Methods Results Gaps ref

  • A. D. De

Andrés et. al (2014) Evaluating different fixed shape models by various wave directions. A triangular shape with different wave directions and a square shape with a unidirectional wave are the best. Limited shape of array [1] C.J.Sharp (2015) Tuned GA Optimal 5-buoy layout with q- factor=1.024 (Best) Simpler model, >37000 (evaluations) [2] Wu et. al (2016) (1+1)EA and (2+2)CMA-EA Optimal 25-buoy layout (q-factor= 0.9 and 100-buoy layout (q-factor= 0.74. One wave frequency and one wave direction [3] Sharp et. al. (2018) Tuned GA Optimises cost and energy output – discrete grid placement – high and low intervals. 5-buoy layout, [11]

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SLIDE 19

The Fitness Function

  • The fitness function is computationally intensive.
  • Each evaluation calculates:

For each buoy

For each wave frequency

For each wave direction (lookup wave-height distribution) For each other buoy in the farm Model Hydrodynamics and estimate energy

  • Can take up to 9 minutes for a full evaluation

– 16 buoys on 12 cores

GECCO ‘18 Optimisation and Logistics Group Slide 20

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Constraints

GECCO ‘18 Optimisation and Logistics Group Slide 21

  • 1) Upper bound for the farm area
  • 2) safe distance between buoys
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Optimisation Targets

GECCO ‘18 Optimisation and Logistics Group Slide 22

4 Buoy Farm 16 Buoy Farm

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Optimisation Setup

  • 13 CPU machine
  • Depending on optimisation meta-heuristic, either:

– Evaluate individual layouts in parallel – Evaluate wave frequencies in parallel

GECCO ‘18 Optimisation and Logistics Group Slide 23

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SLIDE 23

Meta-Heuristics (1)

GECCO ‘18 Optimisation and Logistics Group Slide 24

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Meta-Heuristics (1)

GECCO ‘18 Optimisation and Logistics Group Slide 25

Random Search – place all n buoys simultaneously

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SLIDE 25

Meta-Heuristics (1)

GECCO ‘18 Optimisation and Logistics Group Slide 26

Partial Evaluation – estimate fitness on random subset of frequencies – saves time! Non elitist!

Duc-Cuong Dang and Per Kristian Lehre. 2014. Evolution under partial information. In Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation (GECCO '14). ACM, New York

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SLIDE 26

Meta-Heuristics (1)

GECCO ‘18 Optimisation and Logistics Group Slide 28

TDA – used for wind-turbine placement.

Wagner, Markus & Day, Jareth & Neumann, Frank. (2012). A Fast and Effective Local Search Algorithm for Optimizing the Placement of Wind Turbines. Renewable Energy. 51(2013), 64–70

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SLIDE 27

Meta-Heuristics (1)

GECCO ‘18 Optimisation and Logistics Group Slide 29

CMA ES – custom and from previous work in field.

Junhua Wu, Slava Shekh, Nataliia Y Sergiienko, Benjamin S Cazzolato, Boyin Ding, Frank Neumann, and

  • MarkusWagner. Fast and effective optimisation of arrays of submerged wave energy converters. In GECCO

2016, ACM, 1045–1052.

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SLIDE 28

Meta-Heuristics (1)

GECCO ‘18 Optimisation and Logistics Group Slide 30

Differential Evolution

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SLIDE 29

Meta-Heuristics (1)

GECCO ‘18 Optimisation and Logistics Group Slide 31

1+1 EA’s with different mutation strategies

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Meta-Heuristics (2)

GECCO ‘18 Optimisation and Logistics Group Slide 32

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SLIDE 31

Meta-Heuristics (2)

GECCO ‘18 Optimisation and Logistics Group Slide 33

Buoy at-a-time placement – starts fast, finishes slow.

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SLIDE 32

Meta-Heuristics (2)

GECCO ‘18 Optimisation and Logistics Group Slide 34

Random local neighbourhood search

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SLIDE 33

Meta-Heuristics (2)

GECCO ‘18 Optimisation and Logistics Group Slide 35

Random placement + downhill search on all buoys.

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SLIDE 34

Meta-Heuristics (2)

GECCO ‘18 Optimisation and Logistics Group Slide 36

Alternate placement and downhill search

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SLIDE 35

Meta-Heuristics (2)

GECCO ‘18 Optimisation and Logistics Group Slide 37

Smart offsets for placement of next buoy – different local search and refinements.

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Why Smart?

  • Local landscape (impact of adding second buoy)…

GECCO ‘18 Optimisation and Logistics Group Slide 38

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Comparing Algorithm Performance

  • Methods have different number of wave frequency

evaluations and different placement strategies.

  • Not fair to measure just in terms of evaluations.
  • Most practical measure is the performance of the

best layouts for each algorithm – dedicated machine – with a fixed runtime

  • Runtime – 3 days – 13 processors

GECCO ‘18 Optimisation and Logistics Group Slide 39

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SLIDE 38

Results: Energy – 4 Buoy layout

GECCO ‘18 Optimisation and Logistics Group Slide 41

Many high-performing heuristics – capacity factor > 1

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Layout– 4 Buoys

GECCO ‘18 Optimisation and Logistics Group Slide 43

Different heuristics – similar shaped layout

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Results: Energy – 16 Buoy layout

GECCO ‘18 Optimisation and Logistics Group 44

More challenging and constrained problem

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SLIDE 41

Results: Energy – 16 Buoy layout

GECCO ‘18 Optimisation and Logistics Group 45

Smart local search wins

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…But Partial Evaluation is OK

GECCO ‘18 Optimisation and Logistics Group 46

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…but Partial Evaluation is OK

GECCO ‘18 Optimisation and Logistics Group 47

Small populations or frequencies do well

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Partial Evaluation Traces – 16 buoys

GECCO ‘18 Optimisation and Logistics Group Slide 48

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SLIDE 45

Partial Evaluation Traces – 16 buoys

GECCO ‘18 Optimisation and Logistics Group Slide 49

Small numbers of frequencies converge better.

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SLIDE 46

Traces for Population Based Methods

GECCO ‘18 Optimisation and Logistics Group Slide 50

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SLIDE 47

Traces for 1+1EAs and Smart Placements

GECCO ‘18 Optimisation and Logistics Group Slide 51

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SLIDE 48

Best 16-buoy layout

GECCO ‘18 Optimisation and Logistics Group Slide 52

The best layout of LS_1+NM_2Dfor 16 buoy

  • research. The area size is

566m x 566m, the q- factor=0.956, total power

  • utput 7608600W,
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SLIDE 49

Conclusions

  • Wave Energy Farm Design is a challenging problem.
  • The computational budget is very limited
  • Any informed tricks that can be used to help reduce

this budget can help.

GECCO ‘18 Optimisation and Logistics Group Slide 53

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SLIDE 50

Future Work

  • Explore value of harnessing PE at the start of the

search process – And being selective about frequencies.

  • Explore smart problem initialization.
  • Explore improved models and site-specific wave

energy regimes

  • Look at practicality of sharing anchors.

GECCO ‘18 Optimisation and Logistics Group Slide 54

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SLIDE 51

References

55

  • 1. De Andrés, A. D., Guanche, R., Meneses, L., Vidal, C., & Losada, I. J. (2014). Factors that influence array layout on wave

energy farms. Ocean Engineering, 82, 32-41.

  • 2. Sharp, C., & DuPont, B. (2015, August). Wave Energy Converter Array Optimization: A Review of Current Work and

Preliminary Results of a Genetic Algorithm Approach Introducing Cost Factors. In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference . American Society of Mechanical Engineers.

  • 3. Wu, J., Shekh, S., Sergiienko, N. Y., Cazzolato, B. S., Ding, B., Neumann, F., & Wagner, M. (2016, July). Fast and effective
  • ptimisation of arrays of submerged wave energy converters. In Proceedings of the Genetic and Evolutionary Computation

Conference 2016 (pp. 1045-1052). ACM.

  • 4. Mercadé Ruiz, P., Ferri, F., & Kofoed, J. P. (2017). Experimental validation of a wave energy converter array

hydrodynamics tool. Sustainability, 9(1), 115.

  • 5. Duc-Cuong Dang and Per Kristian Lehre. 2016. Runtime analysis of non-elitist populations: From classical optimisation

to partial information. Algorithmica 75, 3 (2016), 428–461.

  • 6. Markus Wagner, Jareth Day, and Frank Neumann. 2013. A fast and effective local search algorithm for optimizing the

placement of wind turbines. Renewable Energy 51 (2013), 64–70.

  • 7. Nikolaus Hansen. 2006. The CMA evolution strategy: a comparing review. Towards a new evolutionary computation

(2006), 75–102.

  • 8. Junhua Wu, Slava Shekh, Nataliia Y Sergiienko, Benjamin S Cazzolato, Boyin Ding, Frank Neumann, and MarkusWagner.
  • 2016. Fast and effective optimisation of arrays of submerged wave energy converters. In Proceedings of the 2016 on

Genetic and Evolutionary Computation Conference. ACM, 1045–1052.

  • 9. Rainer Storn and Kenneth Price. 1997. Differential evolution–a simple and efficient heuristic for global optimization
  • ver continuous spaces. Journal of global optimization 11, 4 (1997), 341–359.
  • 10. Aguston Eiben, Zbigniew Michalewicz, Marc Schoenauer, and Jim Smith. 2007. Parameter control in evolutionary
  • algorithms. Parameter setting in evolutionary algorithms (2007), 19–46.
  • 11. Sharp, C. and DuPont, B., 2018. Wave energy converter array optimization: A genetic algorithm approach and

minimum separation distance study. Ocean Engineering, 163, pp.148-156.

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SLIDE 52

Questions?

GECCO ‘18 Optimisation and Logistics Group Slide 56