Abstract
Many Renewable Energy Sources (RESs) need to be installed and integrated into the existing grid infrastructure for further developing the energy system towards a 100% RESs supply. To provide the required flexibility for actively compensating local volatile power fluctuations caused e.g. by RES, scheduling of controllable Distributed Energy Resources (DERs) using the concept of Energy Hubs (EHs) and their integration into power networks is a promising approach. The complexity of optimized operation of such EHs while simultaneously providing the required flexibility can be faced by using Evolutionary Algorithms (EAs) for calculating the schedules of internal components of the EHs. To focus the computational effort of the used EA on more important time segments of the problem space, a dynamic optimization method with adaptive time intervals and time resolution is presented. The method improves the quality of the optimization in form of better approximation to a given target value an EH can follow. The proposed approach leads to an average reduction of 11% in the Root Mean Squared Error (RMSE) between target value and measurement by appropriate increased calculation time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
https://unfccc.int/sites/default/files/english_paris_agreement.pdf visited 14.04.2022.
References
Bao, Z., Zhou, Q., Yang, Z., Yang, Q., Xu, L., Wu, T.: A multi time-scale and multi energy-type coordinated microgrid scheduling solution-part i: model and methodology. IEEE Trans. Power Syst. 30, 2257–2266 (2015). https://doi.org/10.1109/TPWRS.2014.2367127
Blume, C., Jakob, W.: Gleam - general learning evolutionary algorithm and method: Ein evolutionärer algorithmus und seine anwendungen. Schriftenreihe des Instituts für Angewandte Informatik - Automatisierungstechnik, Universität Karlsruhe (TH), vol. 32. KIT Scientific Publishing (2009). https://doi.org/10.5445/KSP/1000013553
Cheng, S., Wang, R., Xu, J., Wei, Z.: Multi-time scale coordinated optimization of an energy hub in the integrated energy system with multi-type energy storage systems. Sustain. Energy Technol. Assess. 47, 101327 (2021). https://doi.org/10.1016/j.seta.2021.101327
Fiorini, L., Aiello, M.: Energy management for user’s thermal and power needs: a survey. Energy Rep. 5, 1048–1076 (2019). https://doi.org/10.1016/j.egyr.2019.08.003
Geidl, M., Andersson, G.: A modeling and optimization approach for multiple energy carrier power flow, pp. 1–7 (2005). https://doi.org/10.1109/PTC.2005.4524640
Geidl, M., Andersson, G.: Optimal power flow of multiple energy carriers. IEEE Trans. Power Syst. 22(1), 145–155 (2007). https://doi.org/10.1109/TPWRS.2006.888988
Geidl, M., Koeppel, G., Favre-Perrod, P., Klockl, B., Andersson, G., Frohlich, K.: Energy hubs for the future. IEEE Power Energ. Mag. 5(1), 24–30 (2007). https://doi.org/10.1109/MPAE.2007.264850
Jakob, W., Quinte, A., Stucky, K.-U., Süß, W.: Fast multi-objective scheduling of jobs to constrained resources using a hybrid evolutionary algorithm. In: Rudolph, G., Jansen, T., Beume, N., Lucas, S., Poloni, C. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 1031–1040. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-87700-4_102
Khalloof, H., et al.: A generic distributed microservices and container based framework for metaheuristic optimization. In: Proceedings of the Genetic and Evolutionary Conference Companion, Kyoto, Japan, 15–19 July 2018, pp. 1363–1370. Association for Computing Machinery (ACM) (2018). https://doi.org/10.1145/3205651.3208253
Khalloof, H., Jakob, W., Shahoud, S., Duepmeier, C., Hagenmeyer, V.: A generic scalable method for scheduling distributed energy resources using parallelized population-based metaheuristics. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) FTC 2020. AISC, vol. 1289, pp. 1–21. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-63089-8_1
Kurita, A., et al.: Multiple time-scale power system dynamic simulation. IEEE Trans. Power Syst. 8, 216–223 (1993). https://doi.org/10.1109/59.221237
Le, K.D., Day, J.T.: Rolling horizon method: a new optimization technique for generation expansion studies. PAS-101, 3112–3116 (1982). https://doi.org/10.1109/TPAS.1982.317523
Li, C., et al.: A time-scale adaptive dispatch method for renewable energy power supply systems on islands. IEEE Trans. Smart Grid 7, 1069–1078 (2016). https://doi.org/10.1109/TSG.2015.2485664
Maroufmashat, A., Taqvi, S.T., Miragha, A., Fowler, M., Elkamel, A.: Modeling and optimization of energy hubs: a comprehensive review. Inventions 4, 50 (2019)
Mehdi, R.A.: Scheduling deferrable appliances and energy resources of a smart home applying multi-time scale stochastic model predictive control. Sustain. Cities Soc. 32, 338–347 (2017). https://doi.org/10.1016/j.scs.2017.04.006
Poppenborg, R., et al.: Energy hub gas: a multi-domain system modelling and co-simulation approach. In: Proceedings of the 9th Workshop on Modeling and Simulation of Cyber-Physical Energy Systems, no. 12. Association for Computing Machinery (2021). https://doi.org/10.1145/3470481.3472712
Qiu, H., Gu, W., Xu, Y., Zhao, B.: Multi-time-scale rolling optimal dispatch for ac/dc hybrid microgrids with day-ahead distributionally robust scheduling. IEEE Trans. Sustain. Energy 10, 1653–1663 (2019). https://doi.org/10.1109/TSTE.2018.2868548
Xia, S., Ding, Z., Du, T., Zhang, D., Shahidehpour, M., Ding, T.: Multitime scale coordinated scheduling for the combined system of wind power, photovoltaic, thermal generator, hydro pumped storage, and batteries. IEEE Trans. Ind. Appl. 56(3), 2227–2237 (2020). https://doi.org/10.1109/TIA.2020.2974426
Yang, H., Li, M., Jiang, Z., Zhang, P.: Multi-time scale optimal scheduling of regional integrated energy systems considering integrated demand response. IEEE Access 8, 5080–5090 (2020). https://doi.org/10.1109/ACCESS.2019.2963463
Yi, Z., Xu, Y., Gu, W., Wu, W.: A multi-time-scale economic scheduling strategy for virtual power plant based on deferrable loads aggregation and disaggregation. IEEE Trans. Sustain. Energy 11, 1332–1346 (2020). https://doi.org/10.1109/TSTE.2019.2924936
Zafar, R., Ravishankar, J., Fletcher, J.E., Pota, H.R.: Multi-timescale model predictive control of battery energy storage system using conic relaxation in smart distribution grids. IEEE Trans. Power Syst. 33, 7152–7161 (2018). https://doi.org/10.1109/TPWRS.2018.2847400
Acknowledgment
The authors gratefully acknowledge funding by the German Federal Ministry of Education and Research (BMBF) within the Kopernikus Project ENSURE ‘New ENergy grid StructURes for the German Energiewende’.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Poppenborg, R., Khalloof, H., Chlosta, M., Hofferberth, T., Düpmeier, C., Hagenmeyer, V. (2022). Dynamic Optimization of Energy Hubs with Evolutionary Algorithms Using Adaptive Time Segments and Varying Resolution. In: Yin, H., Camacho, D., Tino, P. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2022. IDEAL 2022. Lecture Notes in Computer Science, vol 13756. Springer, Cham. https://doi.org/10.1007/978-3-031-21753-1_50
Download citation
DOI: https://doi.org/10.1007/978-3-031-21753-1_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-21752-4
Online ISBN: 978-3-031-21753-1
eBook Packages: Computer ScienceComputer Science (R0)