Abstract
The aggregation of distributed energy resources in virtual power plants (VPPs) is a feasible approach to overcome entry barriers for energy markets like, e.g., the European Power Exchange SE (EPEX SPOT SE). An increasing number of energy supply companies offer the integration of decentralized units in VPPs aiming to achieve the maximum profit by trading the power produced by the VPP at energy markets. However, the coordination of the generation units’ operational modes (operation schedule) within a VPP as well as the selection of offered market products (product portfolio) are optimization problems that are mutually dependent. In this contribution a method is proposed that allows automating both the optimized composition of the product portfolio and the determination of the matching operation schedule for the VPP, in terms of profit maximization. Application example of the method is the EPEX SPOT SE day-ahead market. The concept of the approach can be roughly described as follows: First of all, machine learning techniques are used to predict the market prices for the trading day. Then, the market forecast is used in combination with feasible schedule samples from the generation units as input for the optimization process. During the optimization a hybrid approach comprising heuristic algorithms, such as simulated annealing or tabu search, and linear optimization is applied. The approach is evaluated using historical market data and intricate simulation models of generation units.
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References
Gurobi Home Page (2016). http://www.gurobi.com. Accessed 12 April 2016
Awerbuch, S., & Preston, A. (Eds.) (1997). The Virtual Utility: Accounting, Technology & Competitive Aspects of the Emerging Industry, Topics in Regulatory Economics and Policy, vol. 26. Kluwer Academic Publishers
Bagemihl, J. (2003). Optimierung eines Portfolios mit hydro-thermischem Kraftwerkspark im börslichen Strom-und Gasterminmarkt. Ph.D. thesis, Universität Stuttgart. http://elib.uni-stuttgart.de/handle/11682/1614. Accessed 11 April 2016
Bradley, S., Hax, A., & Magnanti, T. (1977). Applied Mathematical Programming. Addison-Wesley. http://web.mit.edu/15.053/www/. Accessed 4 April 2016
Bremer, J., & Sonnenschein, M. (2013). Constraint-handling for Optimization with Support Vector Surrogate Models—A Novel Decoder Approach. In J. Filipe & A. L. N. Fred (Eds.), ICAART 2013—Proceedings of the 5th International Conference on Agents and Artificial Intelligence (Vol. 2, pp. 91–100). Barcelona, Spain: SciTePress.
Dantzig, G. B., & Thapa, M. N. (1997). Linear Programming 1: Introduction. Springer.
European Power Exchange (EPEX SPOT SE) (2016). EPEX SPOT Operational Rules. http://www.epexspot.com/en/extras/download-center. Accessed 17 March 2016
Gendreau, M. (2003). An introduction to tabu search. In F. Glover & G. A. Kochenberger (Eds.), Handbook of Metaheuristics (pp. 37–54). Kluwer Academic Publishers.
Gröwe-Kuska, N., Liebscher, A., Lucht, M., Römisch, W., Spangardt, G., & Wegner, I. (2003). Mittelfristige risikoorientierte Optimierung von Strombeschaffungs-Portfolios kleinerer Marktteilnehmer. Humboldt-Universität zu: Berlin. http://www.mathematik.hu-berlin.de/~romisch/papers/drewag7new2.pdf. Accessed 11 April 2016
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I.H. (2009). The WEKA Data Mining Software: An Update. SIGKDD Explorations, 11(1), 10–18. www.cms.waikato.ac.nz/~ml/publications/2009/weka_update.pdf. Accessed 11 April 2016
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680. doi:10.1126/science.220.4598.671.
Mckay, M. D., Beckman, R. J., & Conover, W. J. (2000). A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code. Technometrics, 42(1), 55–61. http://www.jstor.org/stable/1271432. Accessed 11 April 2016
von Oehsen, A. (2012). Entwicklung und Anwendung einer Kraftwerks- und Speichereinsatzoptimierung für die Untersuchung von Energieversorgungsszenarien mit hohem Anteil erneuerbarer Energien in Deutschland. Ph.D. thesis, Universität Kassel). http://nbn-resolving.de/urn:nbn:de:hebis:34-2013050742706. Accessed 11 April 2016
Sonnenschein, M., Appelrath, H. J., Canders, W. R., Henke, M., Uslar, M., Beer, S., Bremer, J., Lünsdorf, O., Nieße, A., Psola, J. H., & Rosinger, C. (2015). Decentralized Provision of Active Power. In: L. Hofmann & M. Sonnenschein (Eds.) Smart Nord—Final Report. Hanover
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Balduin, S. et al. (2017). Dynamic Portfolio Optimization for Distributed Energy Resources in Virtual Power Plants. In: Wohlgemuth, V., Fuchs-Kittowski, F., Wittmann, J. (eds) Advances and New Trends in Environmental Informatics. Progress in IS. Springer, Cham. https://doi.org/10.1007/978-3-319-44711-7_11
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DOI: https://doi.org/10.1007/978-3-319-44711-7_11
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