Analysis of future power grid loading considering uncertainties for application of risk based grid planning
Final Report Abstract
Due to the present transformation towards integration of renewable energy systems as well as the simultaneous dismantling of the conventional power plants, the load increase due to sector coupling and the liberalization of the energy markets, there are potentially significant uncertainties in the future electrical energy system. Thereby, a division of the currently existing uncertainties can be made by means of two classes. Uncertain input parameters of class I concern the uncertainties regarding the design of the generation portfolio and the peak loads, whereas class II represents the uncertainties of power generation and demand itself. For robust grid planning with a medium- to long-term, it is essential to adequately capture and model such uncertainties in the decision-making process. This requires a detailed mathematical-statistical analysis and quantification of the uncertainties including their correlations. Based on this, it is necessary to represent the effects on the load flow by means of methods of probabilistic load flow calculation, since conventional methods of deterministic load flow calculation are no longer sufficient as the only instrument for the design and monitoring of electrical grids. In the framework of this project, an overall model is developed which permits the determination and analysis of future grid loads, taking real uncertainties into account, and the derivation of optimal investment decisions or the evaluation of already scheduled transmission line construction measures. Within the uncertainty analysis and its modeling, a large number of scenarios is created for the uncertainties of class I, based on the existing generation portfolio and available information on conventional power plant planning or power plant dismantling as well as on renewable energy systems. The identified uncertain factors of class II are represented collectively using a copula model based on probability functions, which enables a detailed depiction of the marginal distributions as well as the correlational relationships. By combining the scenarios with the copula model, distribution functions per scenario at each grid node are modeled or a distribution function based on all scenarios per grid node is created. These individual distribution functions per scenario enable the analysis of different uncertain input variables. To investigate the impact of the uncertain input variables on the transmission capacity, existing methods of probabilistic load flow calculation are further developed to consider correlated and multimodally distributed input variables. With the overall model developed in this approach, the effects of multiple uncertain input variables can be investigated comprehensively.
Publications
- “Integrating Real Options Analysis with long-term electricity market models”, Energy Economics Volume 80, Pages 188-205, May 2019
Festner, D. R.; Blanco, G.; Olsina, F.
(See online at https://doi.org/10.1016/j.eneco.2018.12.023) - “Long-term assessment of power capacity incentives by modeling generation investment dynamics under irreversibility and uncertainty”, Energy Policy Volume 137, February 2020
Festner, D. R.; Blanco, G.; Olsina, F.
(See online at https://doi.org/10.1016/j.enpol.2019.111185) - "Development of an open framework for a qualitative and quantitative comparison of power system and electricity grid models for Europe", Elsevier, Renewable and Sustainable Energy Reviews, Vol. 159, Feb. 2022, 112055
Syranidou, C.; Koch, M.; Matthes, B.; Winger, C.; Linßen, J.; Rehtanz, C.; Stolten, D.
(See online at https://doi.org/10.1016/j.rser.2021.112055) - "Experimental verification of smart grid control functions on international grids using a real-time simulator", IET Generation, Transmission and Distribution, 6. May 2022
Palaniappan, R.; Molodchyk, O.; Shariati-Sarcheshmeh, M.; Asmah, M.; Liu, J.; Schlichtherle, T.; Richter, F.; Appiah Kwofie, E.; Rios Festner, D.; Blanco, G.; Mutule, A.; Borscevskis, O.; Rafaat, S.; Li, Y.; Häger, U.; Rehtanz, C.
(See online at https://doi.org/10.1049/gtd2.12486)
