Author:
Brandt Jonathan,Frost Emilie,Ferenz Stephan,Tiemann Paul Hendrik,Bensmann Astrid,Hanke-Rauschenbach Richard,Nieße Astrid
Abstract
AbstractUsing aggregated flexibility from distributed small-scale power devices is an extensively discussed approach to meet the challenges in modern and increasingly stochastic energy systems. It is crucial to be able to model and map the flexibility of the respective power devices in a unified form to increase the value of the cumulative flexibility from different small-scale power devices by aggregation. In order to identify the most suitable approach for unified flexibility modeling we present a framework to evaluate and compare the advantages and disadvantages of already existing modeling approaches in different levels of detail. As an introduction to flexibility modeling and as a basis for the evaluation process we initially provide a comprehensive overview of the broad range of flexibility models described in scientific literature. Subsequently, five selected modeling approaches allowing the generation of a unified flexibility representation for different power devices are presented in detail. By using an evaluation metric we assess the suitability of the selected approaches for unified flexibility modeling and their applicability. To allow a more detailed performance analysis, the best evaluated models are implemented and simulations with different small-scale devices are performed. The results shown in this paper highlight the heterogeneity of modeling concepts deriving from the various interpretations of flexibility in scientific literature. Due to the varying complexity of the modeling approaches, different flexibility potentials are identified, necessitating a combination of approaches to capture the entire spectrum of the flexibility of different small-scale power devices. Furthermore, it is demonstrated that a complex model does not necessarily lead to the discovery of higher flexibility potentials, and recommendations are given on how to choose an appropriate model.
Funder
Lower Saxony Ministry of Science and Culture
Gottfried Wilhelm Leibniz Universität Hannover
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Energy Engineering and Power Technology,Information Systems
Reference32 articles.
1. Baringo L (2020) Virtual Power Plants and Electricity Markets. Springer, Switzerland. https://doi.org/10.1007/978-3-030-47602-1
2. Barth L, Ludwig N, Mengelkamp E, Staudt P (2018) A comprehensive modelling framework for demand side flexibility in smart grids. Computer Sci Res Develop 33(1–2):13–23. https://doi.org/10.1007/s00450-017-0343-x
3. Boehm, M., Dannecker, L., Doms, A., Dovgan, E., Filipič, B., Fischer, U., Lehner, W., Pedersen, T.B., Pitarch, Y., Šikšnys, L., Tušar, T (2012) Data Management in the MIRABEL Smart Grid System. In: Proceedings of the 2012 Joint EDBT/ICDT Workshops, EDBT-ICDT’12, pp. 95–102. ACM, Berlin, Germany . https://doi.org/10.1145/2320765.2320797. ACM. http://vbn.aau.dk/files/183438910/endm2012_mirabel.pdf
4. Bremer J, Sonnenschein M (2014) Constraint-handling with support vector decoders. Agents Artif Intell. https://doi.org/10.1007/978-3-662-44440-5_14
5. Bremer J, Sonnenschein M (2013) Sampling the Search Space of Energy Resources for Self-organized, Agent-based Planning of Active Power Provision. In: 27th International Conference on Environmental Informatics for Environmental Protection, EnviroInfo 2013, pp 214–222
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献