Developing a Multiple-Objective Demand Response Algorithm for the Residential Context

  • Dennis BehrensEmail author
  • Thorsten Schoormann
  • Ralf Knackstedt
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 320)


Energy grids are facing various challenges, such as new appliances and volatile generation. As grid reliability and cost benefits are endangered, managing appliances becomes increasingly important. Demand Response (DR) is one possibility to contribute to this task by shifting and managing electrical loads. DR can address multiple objectives. However, current research lacks of algorithms addressing these objectives sufficiently. Thus, we aim to develop a DR algorithm that considers multiple DR objectives. For evaluation, we implemented the algorithm and formulated demonstration cases for a simulation. The evaluated algorithm contributes for example to users and energy providers by realizing various benefits.


Demand Response Demand side management Algorithm engineering Greedy heuristic Optimization 


  1. 1.
    Lawrence, T.M., Watson, R.T., Boudreau, M.-C., Mohammadpour, J.: Data flow requirements for integrating smart buildings and a smart grid through model predictive control. Procedia Eng. 180, 1402–1412 (2017)CrossRefGoogle Scholar
  2. 2.
    Seidel, S., Recker, J., vom Brocke, J.: Sensemaking and sustainable practicing: functional affordances of information systems in green transformations. Manag. Inf. Syst. Q. 37, 1275–1299 (2013)CrossRefGoogle Scholar
  3. 3.
    Siano, P.: Demand response and smart grids—a survey. Renew. Sustain. Energy Rev. 30, 461–478 (2014)CrossRefGoogle Scholar
  4. 4.
    Hu, Q., Li, F.: Hardware design of smart home energy management system with dynamic price response. IEEE Trans. Smart Grid. 4, 1878–1887 (2013)CrossRefGoogle Scholar
  5. 5.
    Jovanovic, R., Bousselham, A., Bayram, I.S.: Residential demand response scheduling with consideration of consumer preferences. Appl. Sci. 6, (2016)CrossRefGoogle Scholar
  6. 6.
    Koolen, D., Sadat-Razavi, N., Ketter, W.: Machine learning for identifying demand patterns of home energy management systems with dynamic electricity pricing. Appl. Sci. 7, 1160 (2017)CrossRefGoogle Scholar
  7. 7.
    Kosek, A.M., Costanzo, G.T., Bindner, H.W., Gehrke, O.: An overview of demand side management control schemes for buildings in smart grids. In: 2013 IEEE International Conference on Smart Energy Grid Engineering. SEGE (2013)Google Scholar
  8. 8.
    Merkert, L., Harjunkoski, I., Isaksson, A., Säynevirta, S., Saarela, A., Sand, G.: Scheduling and energy – industrial challenges and opportunities. Comput. Chem. Eng. 72, 183–198 (2015)CrossRefGoogle Scholar
  9. 9.
    Steen, D., Le, T., Bertling, L.: Price-based demand-side management for reducing peak demand in electrical distribution systems – with examples from gothenburg. Chalmers Publication Library (CPL) (2012)Google Scholar
  10. 10.
    Hillemacher, L.: Lastmanagement mittels dynamischer Strompreissignale bei Haushaltskunden. (2014)Google Scholar
  11. 11.
    Vidal, A.R.S., Jacobs, L.A.A., Batista, L.S.: An evolutionary approach for the demand side management optimization in smart grid. In: 2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG), pp. 1–7 (2014)Google Scholar
  12. 12.
    Salinas, S., Li, M., Li, P.: Multi-objective optimal energy consumption scheduling in smart grids. IEEE Trans. Smart Grid. 4, 341–348 (2013)CrossRefGoogle Scholar
  13. 13.
    Behrens, D., Schoormann, T., Knackstedt, R.: Developing an algorithm to consider multiple demand response objectives. Eng. Technol. Appl. Sci. Res. (2018)Google Scholar
  14. 14.
    Al-Sumaiti, A.S., Ahmed, M.H., Salama, M.M.A.: Smart home activities: a literature review. Electr. Power Compon. Syst. 42, 294–305 (2014)CrossRefGoogle Scholar
  15. 15.
    Balijepalli, V.S.K.M., Pradhan, V., Khaparde, S.A., Shereef, R.M.: Review of demand response under smart grid paradigm. In: ISGT2011-India, pp. 236–243 (2011)Google Scholar
  16. 16.
    Gerwig, C., Behrens, D., Lessing, H., Knackstedt, R.: Demand side management in residential contexts - a literature review. In: Lecture Notes in Informatics, pp. 93–107 (2015)Google Scholar
  17. 17.
    Ketter, W., Collins, J., Reddy, P.P., Flath, C.M.: The Power Trading Agent Competition. Social Science Research Network, Rochester (2011)Google Scholar
  18. 18.
    Ali, S.Q., Maqbool, S.D., Ahamed, T.P.I., Malik, N.H.: Pursuit algorithm for optimized load scheduling. In: 2012 IEEE International Power Engineering and Optimization Conference Melaka, Malaysia, pp. 193–198 (2012)Google Scholar
  19. 19.
    Keerthisinghe, C., Verbič, G., Chapman, A.C.: Evaluation of a multi-stage stochastic optimisation framework for energy management of residential PV-storage systems. In: 2014 Australasian Universities Power Engineering Conference (AUPEC), pp. 1–6 (2014)Google Scholar
  20. 20.
    Zhao, W., Cooper, P., Perez, P., Ding, L.: Cost-driven residential energy management for adaption of smart grid and local power generation (2014)Google Scholar
  21. 21.
    Huang, Y., Mao, S., Nelms, R.M.: Smooth electric power scheduling in power distribution networks. In: 2012 IEEE Globecom Workshops, pp. 1469–1473 (2012)Google Scholar
  22. 22.
    McNamara, P., McLoone, S.: Hierarchical demand response for peak minimization using dantzig–wolfe decomposition. IEEE Trans. Smart Grid. 6, 2807–2815 (2015)CrossRefGoogle Scholar
  23. 23.
    Verschae, R., Kawashima, H., Kato, T., Matsuyama, T.: A distributed coordination framework for on-line scheduling and power demand balancing of households communities. In: 2014 European Control Conference (ECC), pp. 1655–1662 (2014)Google Scholar
  24. 24.
    De Angelis, F., Boaro, M., Fuselli, D., Squartini, S., Piazza, F., Wei, Q., Wang, D.: Optimal task and energy scheduling in dynamic residential scenarios. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds.) ISNN 2012. LNCS, vol. 7367, pp. 650–658. Springer, Heidelberg (2012). Scholar
  25. 25.
    Bassamzadeh, N., Ghanem, R., Kazemitabar, S.J.: Robust scheduling of smart appliances with uncertain electricity prices in a heterogeneous population. Entergy Build. 84 (2014)CrossRefGoogle Scholar
  26. 26.
    Kim, S.-J., Giannakis, G.B.: Efficient and scalable demand response for the smart power grid. In: 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 109–112 (2011)Google Scholar
  27. 27.
    Song, L., Xiao, Y., van der Schaar, M.: Non-stationary demand side management method for smart grids. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7759–7763 (2014)Google Scholar
  28. 28.
    Berkeley Lab: Distributed Energy Resources Customer Adoption Model (DER-CAM) | Building Microgrid.
  29. 29.
    Batchu, R., Pindoriya, Naran M.: Residential demand response algorithms: state-of-the-art, key issues and challenges. In: Pillai, P., Hu, Y.F., Otung, I., Giambene, G. (eds.) WiSATS 2015. LNICST, vol. 154, pp. 18–32. Springer, Cham (2015). Scholar
  30. 30.
    Cortés-Arcos, T., Bernal-Agustín, J.L., Dufo-López, R., Lujano-Rojas, J.M., Contreras, J.: Multi-objective demand response to real-time prices (RTP) using a task scheduling methodology. Energy 138, 19–31 (2017)CrossRefGoogle Scholar
  31. 31.
    Behrens, D., Schoormann, T., Knackstedt, R.: Towards a taxonomy of constraints in demand-side-management-methods for a residential context. In: Abramowicz, W. (ed.) BIS 2017. LNBIP, vol. 288, pp. 283–295. Springer, Cham (2017). Scholar
  32. 32.
    Behrens, D., Ruether, C., Schoormann, T., Ambrosi, K., Knackstedt, R.: Effects of constraints in resdential demand-side-management algorithms - a simulation-based study. In: International Conference on Operation Research. Springer, Berlin (2017)Google Scholar
  33. 33.
    Hevner, A.R., March, S.T., Park, J., Ram, S.: Design science in information systems research. Manag. Inf. Syst. Q. 28, 75–105 (2004)CrossRefGoogle Scholar
  34. 34.
    Sanders, P.: Algorithm engineering – an attempt at a definition. In: Albers, S., Alt, H., Näher, S. (eds.) Efficient Algorithms. LNCS, vol. 5760, pp. 321–340. Springer, Heidelberg (2009). Scholar
  35. 35.
    Ketter, W., Peters, M., Collins, J., Gupta, A.: competitive benchmarking: an is research approach to address wicked problems with big data and analytics. Manag. Inf. Syst. Q. 40, 1057–1080 (2016)CrossRefGoogle Scholar
  36. 36.
    Füller, K., Ramanath, R., Böhm, M., Krcmar, H.: Decision support for the selection of appropriate customer integration methods. Wirtsch. Proc. 2015 (2015)Google Scholar
  37. 37.
    Nieße, A., Tröschel, M., Sonnenschein, M.: Designing dependable and sustainable smart grids – how to apply algorithm engineering to distributed control in power systems. Environ. Model Softw. 56, 37–51 (2014)CrossRefGoogle Scholar
  38. 38.
    Mohsenian-Rad, A.-H., Wong, V.W., Jatskevich, J., Schober, R., Leon-Garcia, A.: Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Trans. Smart Grid. 1, 320–331 (2010)CrossRefGoogle Scholar
  39. 39.
    Nelder, J.A., Mead, R.: A simplex method for function minimization. Comput. J. 7, 308–313 (1965)MathSciNetCrossRefGoogle Scholar
  40. 40.
    Land, A.H., Doig, A.G.: An automatic method of solving discrete programming problems. Econometrica 28, 497–520 (1960)MathSciNetCrossRefGoogle Scholar
  41. 41.
    Monacchi, A., Egarter, D., Elmenreich, W., D’Alessandro, S., Tonello, A.M.: GREEND: an energy consumption dataset of households in Italy and Austria. ArXiv14053100 (2014)Google Scholar
  42. 42.
    Behrens, D., Schoormann, T., Knackstedt, R.: Datensets für Demand-Side-Management – Literatur-Review-Basierte Analyse und Forschungsagenda. In: Mayr, H.C., Pinzger, M. (eds.) Lecture Notes in Informatics (2016)Google Scholar
  43. 43.
    Cao, H.Â., Beckel, C., Staake, T.: Are domestic load profiles stable over time? An attempt to identify target households for demand side management campaigns. In: IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society, pp. 4733–4738 (2013)Google Scholar
  44. 44.
    Hoogsteen, G., Molderink, A., Hurink, J.L., Smit, G.J.M.: Generation of flexible domestic load profiles to evaluate demand side management approaches. In: 2016 IEEE International Energy Conference (ENERGYCON), pp. 1–6 (2016)Google Scholar
  45. 45.
    Noah Pflugradt: LoadProfileGenerator.
  46. 46.
    Behrens, D., Gerwig, C., Knackstedt, R., Lessing, H.: Selbstregulierende Verbraucher im Smart Grid: Design einere Infrastruktur mit Hilfe eines Multi-Agenten-Systems. In: Proceedings of the Multikonferenz Wirtschaftsinformatik 2014 (2014)Google Scholar
  47. 47.
    Miao, H., Huang, X., Chen, G.: A genetic evolutionary task scheduling method for energy efficiency in smart homes. Int. Rev. Electr. Eng. 7, 5897–5904 (2012)Google Scholar
  48. 48.
    Soliman, H.M., Leon-Garcia, A.: Game-theoretic demand-side management with storage devices for the future smart grid. IEEE Trans. Smart Grid. 5, 1475–1485 (2014)CrossRefGoogle Scholar
  49. 49.
    Alam, M.R., St-Hilaire, M., Kunz, T.: Cost optimization via rescheduling in smart grids—a linear programming approach. In: 2013 IEEE International Conference on Smart Energy Grid Engineering (SEGE), pp. 1–6 (2013)Google Scholar
  50. 50.
    Maqbool, S.D., Ahamed, T.P.I., Ali, S.Q., Pazheri, F.R., Malik, N.H.: Comparison of pursuit and ε-greedy algorithm for load scheduling under real time pricing. In: 2012 IEEE International Conference on Power and Energy (PECon), pp. 515–519 (2012)Google Scholar
  51. 51.
    Bergstra, J.S., Bardenet, R., Bengio, Y., Kégl, B.: Algorithms for hyper-parameter optimization. In: Advances in Neural Information Processing Systems (2011)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Dennis Behrens
    • 1
    Email author
  • Thorsten Schoormann
    • 1
  • Ralf Knackstedt
    • 1
  1. 1.Department of Information SystemsUniversity of HildesheimHildesheimGermany

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