Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Dynamic Phenotype Mapping in Evolutionary Algorithms for Energy Hub Scheduling

  • Conference paper
  • First Online:
Energy Informatics (EI.A 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15272))

Included in the following conference series:

  • 67 Accesses

Abstract

The integration of Renewable Energy Resources into the existing electricity grid to reduce Greenhouse Gas emissions raises several challenges, such as volatile generation. Optimized scheduling of Distributed Energy Resources (DERs) within the Energy Hub concept can address these challenges by increasing the flexibility in the grid. However, this scheduling task can be categorized as an NP-hard optimization problem and requires the use of powerful heuristic algorithms to solve it. One such heuristic approach is an Evolutionary Algorithm (EA), however, EAs solution quality may be poor w.r.t. solution time when considering complex scheduling tasks of DERs. In our work, we improve the applied EA optimization by considering the predicted optimization quality. More specifically, we use Machine Learning (ML) algorithms trained on previous solutions to forecast the optimization quality. Based on these predictions, the computational effort of the EA is directed to particularly difficult areas of the search space. We direct the effort of the EA by dynamic interval length assignment during the phenotype mapping of the solutions proposed by the EA. We evaluate our approach by comparing multiple ML forecast algorithms and show that our approach leads to a significant increase of the evaluated degree of fulfillment by up to 4.4%.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 74.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://github.com/KIT-IAI/Gleam.

  2. 2.

    https://gitlab.kit.edu/kit/iai/it4es/dynamic-phenotype-mapping-in-evolutionary-algorithms-for-energy-hub-scheduling.

References

  1. Ahmed, A., Khalid, M.: A review on the selected applications of forecasting models in renewable power systems. Renew. Sustain. Energy Rev. 100, 9–21 (2019)

    Article  Google Scholar 

  2. Appino, R.R., González-Ordiano, J.Á., Mikut, R., Faulwasser, T., Hagenmeyer, V.: On the use of probabilistic forecasts in scheduling of renewable energy sources coupled to storages. Appl. Energy 210, 1207–1218 (2018)

    Article  Google Scholar 

  3. 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(5), 2257–2266 (2015). https://doi.org/10.1109/TPWRS.2014.2367127

    Article  Google Scholar 

  4. Beiter, J.: PyTorch forecasting documentation (2020). https://pytorch-forecasting.readthedocs.io/en/stable/, Accessed 07 Nov 2023

  5. Bentley, P.J., Lim, S.L., Gaier, A., Tran, L.: Coil: constrained optimization in learned latent space–learning representations for valid solutions. arXiv preprint arXiv:2202.02163 (2022)

  6. Bentley, P.J., Lim, S.L., Gaier, A., Tran, L.: Evolving through the looking glass: learning improved search spaces with variational autoencoders. In: Rudolph, G., Kononova, A.V., Aguirre, H., Kerschke, P., Ochoa, G., Tušar, T. (eds.) Parallel Problem Solving from Nature - PPSN XVII, PPSN 2022, LNCS, vol. 13398, pp. 371–384. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-14714-2_26

  7. Bishop, C.M., Nasrabadi, N.M.: Pattern recognition and machine learning, vol. 4. Springer, New York (2006)

    Google Scholar 

  8. Blume, C., Jakob, W.: GLEAM - an evolutionary algorithm for planning and control based on evolution strategy. In: Cantú-Paz, E. (ed.) Late Breaking papers at the Genetic and Evolutionary Computation Conference (GECCO-2002), New York, USA, 9-13 July 2002, pp. 31–38. AAAI (2002)

    Google Scholar 

  9. Blume, C., Jakob, W.: GLEAM - general learning evolutionary algorithm and method: EIN evolutionärer Algorithmus und seine Anwendungen. Technical Report, Karlsruhe Institute for Technology (KIT) (2009). https://doi.org/10.5445/KSP/1000013553

  10. Brucker, P.: Computational Complexity, pp. 37–60. Springer, Berlin (2007). https://doi.org/10.1007/978-3-540-69516-5_3

  11. Cantú-Paz, E.: Efficient and Accurate Parallel Genetic Algorithms. Springer US, Boston, MA (2001). https://doi.org/10.1007/978-1-4615-4369-5

  12. Challu, C., Olivares, K.G., Oreshkin, B.N., Garza, F., Mergenthaler, M., Dubrawski, A.: N-hits: neural hierarchical interpolation for time series forecasting. arXiv preprint arXiv:2201.12886 (2022)

  13. Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 785–794. ACM, New York, NY, USA (2016). https://doi.org/10.1145/2939672.2939785

  14. 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

  15. D’Agostino, R., Pearson, E.S.: Tests for departure from normality. empirical results for the distributions of \(b2\) and \(\sqrt b1\). Biometrika 60(3), 613–622 (1973). http://www.jstor.org/stable/2335012

  16. Deb, K.: An efficient constraint handling method for genetic algorithms. Comput. Methods Appl. Mech. Eng. 186(2–4), 311–338 (2000)

    Article  Google Scholar 

  17. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  18. Ding, T., Jia, W., Shahidehpour, M., Han, O., Sun, Y., Zhang, Z.: Review of optimization methods for energy hub planning, operation, trading, and control. IEEE Trans. Sustain. Energy 13(3), 1802–1818 (2022). https://doi.org/10.1109/TSTE.2022.3172004

    Article  Google Scholar 

  19. Distributed (Deep) Machine Learning Community: XGBoost Documentation (2022). https://xgboost.readthedocs.io/en/stable/, Accessed 07 Nov 2023

  20. Efron, B., Hastie, T.: Computer Age Statistical Inference: Algorithms, Evidence, and Data Science. Institute of Mathematical Statistics Monographs, Cambridge University Press (2016). https://doi.org/10.1017/CBO9781316576533

  21. Elmachtoub, A.N., Liang, J.C.N., McNellis, R.: Decision trees for decision-making under the predict-then-optimize framework. In: International Conference on Machine Learning, pp. 2858–2867. PMLR (2020)

    Google Scholar 

  22. Geidl, M.: Integrated modeling and optimization of multi-carrier energy systems. Ph.D. thesis, ETH Zürich (2007). https://doi.org/10.3929/ethz-a-005377890

  23. Geidl, M., Andersson, G.: A modeling and optimization approach for multiple energy carrier power flow. In: 2005 IEEE Russia Power Tech, pp. 1–7 (2005). https://doi.org/10.1109/PTC.2005.4524640

  24. 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

    Article  Google Scholar 

  25. 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

    Article  Google Scholar 

  26. González-Ordiano, J.Á., et al.: Probabilistic forecasts of the distribution grid state using data-driven forecasts and probabilistic power flow. Appl. Energy 302, 117498 (2021). https://doi.org/10.1016/j.apenergy.2021.117498

    Article  Google Scholar 

  27. Gorges-Schleuter, M.: Explicit parallelism of genetic algorithms through population structures. In: Schwefel, H.-P., Männer, R. (eds.) PPSN 1990. LNCS, vol. 496, pp. 150–159. Springer, Heidelberg (1991). https://doi.org/10.1007/BFb0029746

    Chapter  Google Scholar 

  28. Gorges-Schleuter, M.: A comparative study of global and local selection in evolution strategies. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 367–377. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0056879

    Chapter  Google Scholar 

  29. Heidrich, B., et al.: pyWATTS: python workflow automation tool for time series. ArXiv abs/2106.10157 (2021)

    Google Scholar 

  30. Jakob, W., Ordiano, J.A.G., Ludwig, N., Mikut, R., Hagenmeyer, V.: Towards coding strategies for forecasting-based scheduling in smart grids and the Energy Lab 2.0. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2017, pp. 1271–1278. Association for Computing Machinery, New York, NY, USA (2017). https://doi.org/10.1145/3067695.3082481

  31. 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

    Chapter  Google Scholar 

  32. Jakob, W., Strack, S., Quinte, A., Bengel, G., Stucky, K.U., Süß, W.: Fast rescheduling of multiple workflows to constrained heterogeneous resources using multi-criteria memetic computing. Algorithms 6(2), 245–277 (2013). https://doi.org/10.3390/a6020245

    Article  Google Scholar 

  33. 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, J, 15-19 July 2018, pp. 1363–1370. Association for Computing Machinery (ACM) (2018). https://doi.org/10.1145/3205651.3208253

  34. 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

    Chapter  Google Scholar 

  35. Kurita, A., et al.: Multiple time-scale power system dynamic simulation. IEEE Trans. Power Syst. 8(1), 216–223 (1993). https://doi.org/10.1109/59.221237

    Article  Google Scholar 

  36. Le, K.D., Day, J.T.: Rolling horizon method: a new optimization technique for generation expansion studies. IEEE Trans. Power Apparatus Syst. PAS-101(9), 3112–3116 (1982). https://doi.org/10.1109/TPAS.1982.317523

  37. Li, C., et al.: A time-scale adaptive dispatch method for renewable energy power supply systems on islands. IEEE Trans. Smart Grid 7(2), 1069–1078 (2016). https://doi.org/10.1109/TSG.2015.2485664

    Article  Google Scholar 

  38. Lim, B., Arık, S.Ö., Loeff, N., Pfister, T.: Temporal fusion transformers for interpretable multi-horizon time series forecasting. Int. J. Forecast. 37(4), 1748–1764 (2021)

    Article  Google Scholar 

  39. Lösch, M.: Utilization of electric prosumer flexibility incentivized by spot and balancing markets. Ph.D. thesis, Karlsruhe Institute of Technology (KIT) (2022). https://doi.org/10.5445/IR/1000152126

  40. Mehdi, R.A.: Scheduling deferrable appliances and energy resources of a smart home applying multi-time scale stochastic model predictive control. Sustain. Urban Areas 32, 338–347 (2017). https://doi.org/10.1016/j.scs.2017.04.006

    Article  Google Scholar 

  41. Nemati, M., Braun, M., Tenbohlen, S.: Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming. Appl. Energy 210, 944–963 (2018). https://doi.org/10.1016/j.apenergy.2017.07.007

    Article  Google Scholar 

  42. Pedregosa, F., et al.: Scikit-learn: Mach. Learn. Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  Google Scholar 

  43. Petropoulos, F., et al.: Forecasting: theory and practice. Int. J. Forecast. 38(3), 705–871 (2022). https://doi.org/10.1016/j.ijforecast.2021.11.001

    Article  Google Scholar 

  44. Poppenborg, R., Beisswanger, K., Hotz, C., Förderer, K., Kolb, T., Hagenmeyer, V.: Dynamic mapping for evolutionary algorithm based optimization of energy hub gas scheduling. In: 2023 IEEE 11th International Conference on Smart Energy Grid Engineering (SEGE). pp. 206–211 (2023). https://doi.org/10.1109/SEGE59172.2023.10274571

  45. Poppenborg, R., et al.: Energy hub gas: a modular setup for the evaluation of local flexibility and renewable energy carriers provision. In: 2022 IEEE 10th International Conference on Smart Energy Grid Engineering (SEGE), pp. 33–41 (2022). https://doi.org/10.1109/SEGE55279.2022.9889751

  46. Poppenborg, R., Khalloof, H., Chlosta, M., Hofferberth, T., Düpmeier, C., Hagenmeyer, V.: 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, LNCS, vol. 13756, pp 513–524. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-21753-1_50

  47. Poppenborg, R., Phipps, K., Khalloof, H., Förderer, K., Mikut, R., Hagenmeyer, V.: Dynamic chromosome interpretation in evolutionary algorithms for distributed energy resources scheduling. In: Proceedings of the Companion Conference on Genetic and Evolutionary Computation, GECCO 2023, pp. 755-758. Companion, Association for Computing Machinery, New York, NY, USA (2023). https://doi.org/10.1145/3583133.3590666

  48. Poppenborg, R., et al.: Energy hub gas: a multi-domain system modelling and co-simulation approach. In: 2021 9th Workshop on Modelling and Simulation of Cyber-Physical Energy Systems (MSCPES), pp. 67–72 (2021). https://doi.org/10.1145/3470481.3472712

  49. 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(4), 1653–1663 (2019). https://doi.org/10.1109/TSTE.2018.2868548

    Article  Google Scholar 

  50. Sayedin, F., Maroufmashat, A., Al-Adwani, S., Khavas, S.S., Elkamel, A., Fowler, M.: Evolutionary optimization approaches for direct coupling photovoltaic-electrolyzer systems. In: 2015 International Conference on Industrial Engineering and Operations Management (IEOM). pp. 1–8 (2015). https://doi.org/10.1109/IEOM.2015.7093884

  51. Schäfer, P., Mitsos, A.: Tailored time grids for nonlinear scheduling subject to time-variable electricity prices by wavelet-based analysis. In: Pierucci, S., Manenti, F., Bozzano, G.L., Manca, D. (eds.) 30th European Symposium on Computer Aided Process Engineering, Computer Aided Chemical Engineering, vol. 48, pp. 1123–1128. Elsevier (2020). https://doi.org/10.1016/B978-0-12-823377-1.50188-9, https://www.sciencedirect.com/science/article/pii/B9780128233771501889

  52. Schäfer, P., Schweidtmann, A.M., Lenz, P.H.A., Markgraf, H.M.C., Mitsos, A.: Wavelet-based grid-adaptation for nonlinear scheduling subject to time-variable electricity prices. Comput. Chem. Eng. 132, 106598 (2020). https://doi.org/10.1016/j.compchemeng.2019.106598, https://juser.fz-juelich.de/record/877550

  53. Son, Y.G., Oh, B.C., Acquah, M.A., Fan, R., Kim, D.M., Kim, S.Y.: Multi energy system with an associated energy hub: a review. IEEE Access 9, 127753–127766 (2021). https://doi.org/10.1109/ACCESS.2021.3108142

  54. Svetnik, V., Liaw, A., Tong, C., Culberson, J.C., Sheridan, R.P., Feuston, B.P.: Random forest: a classification and regression tool for compound classification and QSAR modeling. J. Chem. Inf. Comput. Sci. 43(6), 1947–1958 (2003)

    Article  Google Scholar 

  55. Tang, Z., Fishwick, P.A.: Feedforward neural nets as models for time series forecasting. ORSA J. Comput. 5(4), 374–385 (1993)

    Article  Google Scholar 

  56. Tseng, C.L.: On Power System Generation Unit Commitment Problems. University of California, Berkeley (1996)

    Google Scholar 

  57. 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

    Article  Google Scholar 

  58. 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

    Article  Google Scholar 

  59. 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(3), 1332–1346 (2020). https://doi.org/10.1109/TSTE.2019.2924936

    Article  Google Scholar 

  60. 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(6), 7152–7161 (2018). https://doi.org/10.1109/TPWRS.2018.2847400

    Article  Google Scholar 

Download references

Acknowledgments

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’, the Helmholtz Association’s Initiative and Networking Fund through Helmholtz AI, the Helmholtz Association under the Program “Energy System Design”, and the German Research Foundation (DFG) as part of the Research Training Group, 2153 “Energy Status Data: Informatics Methods for its Collection, Analysis and Exploitation”.

Author information

Authors and Affiliations

  1. Karlsruhe Institute of Technology, Kaiserstr. 12, 76131, Karlsruhe, Germany

    Rafael Poppenborg, Kaleb Phipps, Maximilian Beichter, Kevin Förderer, Ralf Mikut & Veit Hagenmeyer

Authors
  1. Rafael Poppenborg
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kaleb Phipps
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maximilian Beichter
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kevin Förderer
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ralf Mikut
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Veit Hagenmeyer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rafael Poppenborg .

Editor information

Editors and Affiliations

  1. University of Southern Denmark, Odense, Denmark

    Bo Nørregaard Jørgensen

  2. University of Southern Denmark, Odense, Denmark

    Zheng Grace Ma

  3. Universitas Gadjah Mada, Yogyakarta, Indonesia

    Fransisco Danang Wijaya

  4. Universitas Gadjah Mada, Yogyakarta, Indonesia

    Roni Irnawan

  5. Universitas Gadjah Mada, Yogyakarta, Indonesia

    Sarjiya Sarjiya

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Poppenborg, R., Phipps, K., Beichter, M., Förderer, K., Mikut, R., Hagenmeyer, V. (2025). Dynamic Phenotype Mapping in Evolutionary Algorithms for Energy Hub Scheduling. In: Jørgensen, B.N., Ma, Z.G., Wijaya, F.D., Irnawan, R., Sarjiya, S. (eds) Energy Informatics. EI.A 2024. Lecture Notes in Computer Science, vol 15272. Springer, Cham. https://doi.org/10.1007/978-3-031-74741-0_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-74741-0_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-74740-3

  • Online ISBN: 978-3-031-74741-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation