Abstract
In this paper, a reliability-sensitive optimization approach for the provision of ancillary services from distributed energy resources is proposed. The main focus here is on small-scale renewable energy sources such as wind turbines and PV arrays, which are highly distributed, and the generated power is weather dependent and intermittent. Therefore, providing a reliable ancillary service through these unreliable resources is a complicated problem addressed here. In this paper, the tempo-spatial correlation between these renewable resources is mathematically modeled by pair-copula functions and included in the reliability evaluation procedure using the non-sequential Monte Carlo simulation method. An optimization problem is formulated using proposed joint reliability model to find the minimum number of renewable resources to provide ancillary services within a desired reliability level. The proposed optimization model is applied to real wind farms in Lower Saxony in Germany to provide the spinning reserve service. The results prove that including the correlation concept in the reliability evaluation procedure leads to the more realistic scenarios that play an essential role in maintaining the service’s reliability level.
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References
Ni, L., et al.: Vine copula selection using mutual information for hydrological dependence modeling. Environ. Res. 186, 109604 (2020)
Li, J., Zhou, J., Chen, B.: Review of wind power scenario generation methods for optimal operation of renewable energy systems. Appl. Energy 280, 115992 (2020)
Zhang, N., Kang, C., Xia, Q., Liang, J.: Modeling conditional forecast error for wind power in generation scheduling. IEEE Trans. Power Syst. 29(3), 1316–1324 (2013)
Chen, Y., Wang, Y., Kirschen, D., Zhang, B.: Model-free renewable scenario generation using generative adversarial networks. IEEE Trans. Power Syst. 33(3), 3265–3275 (2018)
Li, J., Lan, F., Wei, H.: A scenario optimal reduction method for wind power time series. IEEE Trans. Power Syst. 31(2), 1657–1658 (2015)
Teimourzadeh Baboli, P., Brand, M., Lehnhoff, S.: Stochastic correlation modelling of renewable energy sources for provision of ancillary services using multi-dimensional copula functions. In: 2021 11th Smart Grid Conference (SGC), IEEE, 2021, pp. 1–6 (2021)
Li, B., et al.: Probabilistic analysis to analyze uncertainty incorporating copula theory. J. Electr. Eng. Technol. 17, 1–11 (2021)
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(1), 51–62 (2009)
Becker, R.: Generation of time-coupled wind power infeed scenarios using pair-copula construction. IEEE Trans. Sustain. Energy 9(3), 1298–1306 (2017)
Dissmann, J., Brechmann, E.C., Czado, C., Kurowicka, D.: Selecting and estimating regular vine copulae and application to financial returns. Comput. Stat. Data Anal. 59, 52–69 (2013)
Wang, Z., Wang, W., Liu, C., Wang, Z., Hou, Y.: Probabilistic forecast for multiple wind farms based on regular vine copulas. IEEE Trans. Power Syst. 33(1), 578–589 (2017)
Sun, M., Feng, C., Zhang, J.: Conditional aggregated probabilistic wind power forecasting based on spatio-temporal correlation. Appl. Energy 256, 113842 (2019)
Vatter, T., Nagler, T.: Generalized additive models for pair-copula constructions. J. Comput. Graph. Stat. 27(4), 715–727 (2018)
Kraus, D., Czado, C.: D-vine copula based quantile regression. Comput. Stat. Data Anal. 110, 1–18 (2017)
Zhang, J., Gao, K., Li, Y., Zhang, O.: Maximum likelihood estimation methods for copula modelsa. Computat. Econ. 60, 1–26 (2021)
Acknowledgements
This research has been funded by the Lower Saxony Ministry of Science and Culture through the ‘Niedersächsisches Vorab’ grant programme (grant ZN3563) and of the Energy Research Centre of Lower Saxony through the research project SiNED - Systemdienstleistungen für sichere Stromnetze in Zeiten fortschreitender Energiewende und digitaler Transformation.
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Teimourzadeh Baboli, P., Raeiszadeh, A., Brand, M., Lehnhoff, S. (2023). Reliability-Sensitive Optimization for Provision of Ancillary Services by Tempo-Spatial Correlated Distributed Energy Resources. In: García Bringas, P., et al. 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022). SOCO 2022. Lecture Notes in Networks and Systems, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-031-18050-7_22
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DOI: https://doi.org/10.1007/978-3-031-18050-7_22
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