Abstract
For the integration of renewable energy sources, power grid operators need realistic information about the effects of energy production and consumption to assess grid stability. Recently, research in scenario planning benefits from utilizing generative adversarial networks (GANs) as generative models for operational scenario planning. In these scenarios, operators examine temporal as well as spatial influences of different energy sources on the grid. The analysis of how renewable energy resources affect the grid enables the operators to evaluate the stability and to identify potential weak points such as a limiting transformer. However, due to their novelty, there are limited studies on how well GANs model the underlying power distribution. This analysis is essential because, e.g., especially extreme situations with low or high power generation are required to evaluate grid stability. We conduct a comparative study of the Wasserstein distance, binary-cross-entropy loss, and a Gaussian copula as the baseline applied on two wind and two solar datasets with limited data compared to previous studies. Both GANs achieve good results considering the limited amount of data, but the Wasserstein GAN is superior in modeling temporal and spatial relations, and the power distribution. Besides evaluating the generated power distribution over all farms, it is essential to assess terrain specific distributions for wind scenarios. These terrain specific power distributions affect the grid by their differences in their generating power magnitude. Therefore, in a second study, we show that even when simultaneously learning distributions from wind parks with terrain specific patterns, GANs are capable of modeling these individualities also when faced with limited data. These results motivate a further usage of GANs as generative models in scenario planning as well as other areas of renewable energy.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Implementation details of the evaluation, the experiment, and the training is available at https://git.ies.uni-kassel.de/scenario_gan/scenario_gan_wind_pv.
- 2.
References
Fraunhofer-Institut für Energiewirtschaft und Energiesystemtechnik IEE. Windenergie Report Deutschland 2017 (2018)
Lowery, C., O’Malley, M.: Wind power scenario tree tool: development and methodology. In: Billinton, R., Karki, R., Verma, A. (eds.) Reliability and Risk Evaluation of Wind Integrated Power Systems. RSEPESM, pp. 13–27. Springer, New Delhi (2013). https://doi.org/10.1007/978-81-322-0987-4_2
Schreiber, J., Sick, B.: Quantifying the influences on probabilistic wind power forecasts. In: ICPRE, vol. 3, p. 6 (2018). https://doi.org/10.1051/e3sconf/20186406002
Sovan, M.: White paper on scenario generation for stochastic programming. Technical report (2008)
Hart, E.K., Jacobson, M.Z.: A Monte Carlo approach to generator portfolio planning and carbon emissions assessments of systems with large penetrations of variable renewables. Renew. Energy 36(8), 2278–2286 (2011). https://doi.org/10.1016/j.renene.2011.01.015
Becker, R.: Generation of time-coupled wind power infeed scenarios using pair-copula construction. IEEE Trans. Sustain. Energy 9(3), 1298–1306 (2018). https://doi.org/10.1109/TSTE.2017.2782089
Kaut, M., Wallace, S.W.: Evaluation of scenario-generation methods for stochastic programming. Pac. J. Optim. 3(2), 14 (2003). https://doi.org/10.18452/8296
Chen, Y., Li, P., Zhang, B.: Bayesian renewables scenario generation via deep generative networks. CiSS 52, 6 (2018). https://doi.org/10.1109/CISS.2018.8362314
Chen, Y., Wang, Y., Kirschen, D.S., Zhang, B.: Model-free renewable scenario generation using generative adversarial networks. IEEE Trans. Power Syst. 33(3) (2018). https://doi.org/10.1109/TPWRS.2018.2794541
Chen, Y., Wang, X., Zhang, B.: An unsupervised deep learning approach for scenario forecasts. CoRR, arXiv:1711.02247:7 (2018). https://doi.org/10.23919/PSCC.2018.8442500
Pinson, P.: Estimation of the uncertainty in wind power forecasting. Ph.D. thesis, Ecole des Mines de Paris, Paris (2006)
Schreiber, J., Buschin, A., Sick, B.: Influences in forecast errors for wind and photovoltaic power: a study on machine learning models. arXiv:1905.13668 (2019)
Conejo, A.J., Carrión, M., Morales, J.M.: Decision Making Under Uncertainty in Electricity Markets. ISOR, vol. 153. Springer, Boston (2010). https://doi.org/10.1007/978-1-4419-7421-1
Pinson, P., Madsen, H., Nielsen, H.A., Papaefthymiou, G., Klöckl, B.: From probabilistic forecasts to statistical scenarios of short-term wind power production. Wind Energy 12, 51–62 (2009). https://doi.org/10.1002/we.284
Iversen, J.E.B., Pinson, P.: RESGen: renewable energy scenario generation platform. In: IEEE PES General Meeting, p. 6 (2016)
Pinson, P., Girard, R.: Evaluating the quality of scenarios of short-term wind power generation. Appl. Energy 96, 12–20 (2012). https://doi.org/10.1016/j.apenergy.2011.11.004
Wang, T., Chiang, H.D., Tanabe, R.: Toward a flexible scenario generation tool for stochastic renewable energy analysis. In: Power Systems Computation Conference, pp. 1–7 (2016). https://doi.org/10.1109/PSCC.2016.7540991
Rachunok, B., Staid, A., Watson, J.P., Woodruff, D.L., Yang, D.: Stochastic unit commitment performance considering Monte Carlo wind power scenarios. In: PMAPS, pp. 1–6 (2018). https://doi.org/10.1109/PMAPS.2018.8440563
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Zenati, H., Foo, C.S., Lecouat, B., Manek, G., Chandrasekhar, V.R.: Efficient GAN-based anomaly detection. CoRR, arXiv:1802.06222:7 (2018)
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN. CoRR arXiv:1701.07875 (2017)
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. CoRR arXiv:1511.06434 (2015)
Nelsen, R.B.: An Introduction to Copulas. Springer, New York (2006). https://doi.org/10.1007/0-387-28678-0
Pedregosa, F., Varoquaux, G., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Acknowledgment
This work was supported within the project Prophesy (0324104A) funded by BMWi (Deusches Bundesministerium für Wirtschaft und Energie/German Federal Ministry for Economic Affairs and Energy).
Additionally, special thanks to Maarten Bieshaar for excellent discussions about Gaussian copulas.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Schreiber, J., Jessulat, M., Sick, B. (2019). Generative Adversarial Networks for Operational Scenario Planning of Renewable Energy Farms: A Study on Wind and Photovoltaic. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing. ICANN 2019. Lecture Notes in Computer Science(), vol 11729. Springer, Cham. https://doi.org/10.1007/978-3-030-30508-6_44
Download citation
DOI: https://doi.org/10.1007/978-3-030-30508-6_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30507-9
Online ISBN: 978-3-030-30508-6
eBook Packages: Computer ScienceComputer Science (R0)