Renewable EnergyCoordinator: Dr Markus Wagner
Renewable energy is playing an increasing role in the supply of energy worldwide and will help mitigate climate change. We as computer scientists contribute to the mitigation with the development of algorithmic solutions to a wide range of challenges. For example, we have extensive experience in the design optimisation of wind farms and wave energy farms (see below), and in the development of human-readable prediction models.
In order to promote the research on Computational Intelligence methods for their application to the energy production and consumption domain, members of the Optimisation and Logistics Group co-founded the IEEE CIS Task Force on Computational Intelligence in the Energy Domain. Dr. Markus Wagner was the founding chair of this task force. For more information on the events and special issues that this task force organises, please visit the official website.
Optimisation of Wave Energy ConvertersIn 2015, we have begun to collaborate with Prof. Ben Cazzolato's Wave Energy Converter Group at the School of Mechanical Engineering, The University of Adelaide. This research was seed-funded by the DVCR's Interdisciplinary Research Fund with $27,000, and by the ECMS Faculty with $18,025.
So far, our studies provide first insights into the layout problem of submerged wave energy converters. It is also the first time multiple three tether buoys are being investigated, both in single-objective and multi-objective settings. Most of our work focusses on layout optimisation, to maximise the constructive interference and minimise the destructive interference. Recently, we have begun to incorporate into our layout search the optimisation of the Power Take-Off (PTO). As the evaluations with our frequency-domain model (which is validated against computational fluid dynamics simulations) are computationally costly, we are also exploring the use of machine-learning surrogates.
All publications are listed at the very bottom of this page.
Code: over the years, Nataliia Y. Sergiienko has provided us with a number of refinements and extensions of her implementation of the frequency-domain model. The corresponding Matlab code is available at the MathWorks File Exchange.
Most of the actual optimisation code has been written by Mehdi Neshat, and the source code is available at the MathWorks File Exchange.
Related to our GECCO 2019 article are the following animations. Note that the wave scenarios are very different, which is ultimately reflected in the distribution of the wave energy converters.
For a larger collection of videos, please see our YouTube Playlist. For our listing at Tethys (U.S. Department of Energy), check the Knowledge Base Entry Optimizing the Wave Energy Converters by Metaheuristic Methods.
Layout Optimisation of Large Wind FarmsIn order to further increase the productivity of wind farms, we need to exploit methods that help to optimise their performance.
In case of problems with the video stream, watch it on Youtube.
The question of exactly where wind turbines should be placed to gain maximum efficiency is highly complex. An evolutionary algorithm is a mathematical process where potential solutions keep being improved a step at a time until the optimum is reached. Metaphorically speaking, it is like parents producing a number of offspring, each with differing characteristics. As with evolution, each population or ‘set of solutions’ from a new generation should get better. These solutions can be evaluated in parallel to speed up the computation.
Using the same principles, we can also solve human problems through computer algorithms. Current approaches to solving this placement optimisation can only deal with a small number of turbines, but we have recently demonstrated an accurate and efficient algorithm for as many as 1000 turbines.
Together with researchers at the Massachusetts Institute of Technology, we have been working on wind turbine placement optimisation, and we are now looking to fine-tune the algorithms even further using different models of wake effect and complex aerodynamic factors.
|Demonstration of our Turbine Displacement Algorithm and its infeasible area modelling capability.|
The image on the left portrays a satellite image of the Woolnorth wind farm in Tasmania, Australia (© 2011 Google). The right images are examples of the the loose adaptation used in the modelling tool, and from left to right model the scenario at 0 (16.9 MW), 5,000 (17.6 MW), and 20,000 (17.7 MW) evaluations. As the considered wind is predominantly from the western direction (between 120° to 225°), the turbines tend to form in staggered north/south columns while leaving space along the east/west directions.
SoftwareFor the assessment of wind farm layouts, we implemented the Park wake model as presented in "A. Kusiak and Z. Song: 'Design of Wind Farm layout for Maximum Wind Energy Capture,' Renewable Energy, Vol. 35, No. 3, 2010, pp. 685-694." Our cross-validated implementation is available for download as a Netbeans Java project. In addition to the assessment function, the code contains flexible initializers for different layouts, and correctors for infeasible layouts. (Author: Markus Wagner)
We provide the single-objective code from our Renewable Energy Journal 2013 article here.
We also provide the multi-objective code from our GECCO 2013 article here. The code is based on the popular framework jMetal, and we packaged it with our fitness functions (energy production, minimum spanning tree, convex hull) and our Matlab scripts to generate the plots.
Important note: due to an error in equation 18 of the Kusiak-Song-article, their predictions are exacly 15 times too high. Consequently, our code's predictions are exactly 15 times too high as well.
ConstraintsIn our scenarios, we have the following constraints placed on our optimization function. The first one enforces an upper bound on the area of the farm. This constraint ensures that we can only place a turbine i within a certain area, which is a realistic constraint for most layout problems. For a rectangular farm with length l and width w this constraint is satisfied
In addition to the above constraints, we assume that all turbines have the same power curves (approximated as piecewise linear functions) and that the same wind resource spans the entire farm. To increase accuracy, these resources can be estimated for different parts in the farm. The assumptions can be revised in a very straight forward manner to generate more realistic scenarios.
Usage in ClassroomsWe have used this problem (and this code) as a group project in our course on "Evolutionary Computation". For the results, please click here and here.
Wind turbine power output prediction using a new hybrid neuro-evolutionary method
Mehdi Neshat, Meysam Majidi Nezhad, Ehsan Abbasnejad, Seyedali Mirjalili, Daniele Groppi, Azim Heydari, Lina Bertling Tjernberg, Davide Astiaso Garcia, Bradley Alexander, Qinfeng Shi, Markus Wagner
Energy (IF 6.082)
A deep learning-based evolutionary model for short-term wind speed forecasting: A case study of the Lillgrund offshore wind farm
Mehdi Neshat, Meysam Majidi Nezhad, Ehsan Abbasnejad, Seyedali Mirjalili, Lina Bertling Tjernberg, Davide Astiaso Garcia, Bradley Alexander, Markus Wagner
Energy Conversion and Management (IF 8.208)
A New Bi-Level Optimisation Framework for Optimising a Multi-Mode Wave Energy Converter Design: A Case Study for the Marettimo Island, Mediterranean Sea
Mehdi Neshat, Nataliia Y. Sergiienko, Erfan Amini, Meysam Majidi Nezhad, Davide Astiaso Garcia, Bradley Alexander and Markus Wagner
Energies (IF 2.702)
Hybrid Neuro-Evolutionary Method for Predicting Wind Turbine Power Output
Mehdi Neshat, Meysam Majidi Nezhad, Ehsan Abbasnejad, Daniele Groppi, Zim Heydari, Lina Bertling Tjernberg, Davide Astiaso Garcia, Bradley Alexander, and Markus Wagner
Optimisation of Large Wave Farms using a Multi-strategy Evolutionary Framework
Mehdi Neshat, Bradley Alexander, Nataliia Y. Sergiienko, and Markus Wagner
Best Paper Award (RWA Track)
arxiv.org | animations | pre-recorded GECCO presentation
An Evolutionary Deep Learning Method for Short-term Wind Speed Prediction: A Case Study of the Lillgrund Offshore Wind Farm
Mehdi Neshat, Meysam Majidi Nezhad, Ehsan Abbasnejad, Lina Bertling Tjernberg, Davide Astiaso Garcia, Bradley Alexander, Markus Wagner
Design optimisation of a multi-mode wave energy converter
Nataliia Y. Sergiienko, Mehdi Neshat, Leandro S.P. da Silva, Bradley Alexander and Markus Wagner
Accepted at OMAE 2020 (39th International Conference on Ocean, Offshore & Arctic Engineering)
arxiv.org | simulation tool
[DATASET] UCI Machine Learning Repository: Wave Energy Converters Data Set
This data is related to our following two articles: (1) A new insight into the Position Optimization of Wave Energy Converters by a Hybrid Local Search, (2) A detailed comparison of meta-heuristic methods for optimising wave energy converter placements
A Hybrid Cooperative Co-evolution Algorithm Framework for Optimising Power Take Off and Placements of Wave Energy Converters
Mehdi Neshat, Bradley Alexander, and Markus Wagner
Information Sciences (IF 5.524), arxiv.org | Elsevier
Adaptive Neuro-Surrogate-Based Optimisation Method for Wave Energy Converters Placement Optimisation
Mehdi Neshat, Ehsan Abbasnejad, Qinfeng Shi, Bradley Alexander, and Markus Wagner
International Conference on Neural Information Processing (ICONIP) 2019
arxiv.org | researchgate | SpringerLink
New insights into the Position Optimization of Wave Energy Converters by a Hybrid Local Search
Mehdi Neshat, Bradley Alexander, Nataliia Sergiienko, and Markus Wagner
arxiv.org (accepted at Swarm and Evolutionary Computation journal on 8 July 2020, IF 6.912) | code: Improved Differential Evolution, Improved Smart Local Search & Nelder-Mead, Binary Genetic Algorithm, Heterogeneous comprehensive learning PSO (HCLPSO), Restart CMA evolution strategy with increasing population size (IPOP-CMA-ES)
A Hybrid Evolutionary Algorithm Framework for Optimising Power Take Off and Placements of Wave Energy Converters
Mehdi Neshat, Bradley Alexander, Nataliia Y. Sergiienko, and Markus Wagner
Genetic and Evolutionary Computation Conference (GECCO) 2019
Best Paper Award (RWA Track)
PDF with Appendix | PDF of Presentation Slides | code
Sparse incomplete LU-decomposition for Wave Farm Designs under Realistic Conditions
Didac Rodriguez Arbones, Nataliia Y. Sergiienko, Boyin Ding, Oswin Krause, Christian Igel, and Markus Wagner
Parallel Problem Solving from Nature (PPSN) 2018
A Detailed Comparison of Meta-Heuristic Methods for Optimising Wave Energy Converter Placements
Mehdi Neshat, Bradley Alexander, Markus Wagner, and Yuanzhong Xia
Genetic and Evolutionary Computation Conference (GECCO) 2018
PDF | PDF with appendix | slides | code | animation of Local Search + Nelder-Mead approach (best approach in this paper)
Study of fully submerged point absorber wave energy converter - modelling, simulation and scaled experiment
Boyin Ding, Leandro Souza Pinheiro da Silva, Nataliia Sergiienko, Fantai Meng, Jonathan David Piper, Luke Bennetts, Markus Wagner, Benjamin Cazzolato, Maziar Arjomandi
International Workshop on Water Waves and Floating Bodies (IWWWFB) 2017
Fast and Effective Multi-Objective Optimisation of Submerged Wave Energy Converters
Didac Rodriguez Arbones, Boyin Ding, Nataliia Sergiienko, and Markus Wagner
Proceedings, Parallel Problem Solving from Nature (PPSN 2016)
Fast and effective optimisation of arrays of submerged wave energy converters
Junhua Wu, Slava Shekh, Nataliia Sergiienko, Benjamin Cazzolato, Boyin Ding, Frank Neumann, and Markus Wagner
Proceedings, Genetic and Evolutionary Computation Conference (GECCO 2016)
PDF | Matlab code
Constrained Evolutionary Wind Turbine Placement with Penalty Functions
Daniel Lückehe, Oliver Kramer and Markus Wagner
Proceedings, World Congress on Computational Intelligence: Congress on Evolutionary Computation (WCCI/CEC 2016)
Frequency domain model of the three-tether WECs array
Nataliia Sergiienko, Benjamin Cazzolato, Boyin Ding, and Maziar Arjomandi
Internal technical report (2016)
Renewable Energy 2016 Special Issue "Optimization Methods in Renewable Energy Systems Design"
Paul Kaufmann, Oliver Kramer, Frank Neumann, and Markus Wagner (Managing Guest Editor)
ScienceDirect | Our special issue received 135 submissions.
On Evolutionary Approaches to Wind Turbine Placement with Geo-Constraints
Daniel Lückehe, Markus Wagner, and Oliver Kramer
Proceedings, Genetic and Evolutionary Computation Conference (GECCO 2015)
Fast and Effective Multi-Objective Optimisation of Wind Turbine Placement
Raymond Tran, Junhua Wu, Christopher Denison, Thomas Ackling, Markus Wagner, and Frank Neumann
Proceedings, Genetic and Evolutionary Computation Conference (GECCO 2013)
PDF | complete jMetal source code with fitness functions and Matlab scripts
A Fast and Effective Local Search Algorithm for Optimizing the Placement of Wind Turbines
Markus Wagner, Jareth Day, and Frank Neumann
Elsevier: Renewable Energy | arXiv.org | Code: Turbine Distribution Algorithm (TDA)
Optimizing Energy Output and Layout Costs for Large Wind Farms using Particle Swarm
Kalyan Veeramachaneni, Markus Wagner, Una-May O'Reilly and Frank Neumann
Proceedings, IEEE World Congress on Computational Intelligence: Congress on Evolutionary Computation (WCCI/CEC 2012)
PDF | Abstract
Predicting the Energy Output of Wind Farms Based on Weather Data: Important Variables
and their Correlation
Katya Vladislavleva, Tobias Friedrich, Frank Neumann, and Markus Wagner
Elsevier: Renewable Energy | arXiv.org
Optimizing the Layout of 1000 Wind Turbines
Markus Wagner, Kalyan Veeramachaneni, Frank Neumann, and Una-May O'Reilly
Proceedings, European Wind Energy Association 2011 (EWEA 2011)
PDF | Poster | BibTeX | Abstract