Exploiting flexibility in smart grids at scale

The resource utilization scheduling heuristic

Abstract

Large parts of the worldwide energy system are undergoing drastic changes at the moment. Two of these changes are the increasing share of intermittent generation technologies and the advent of the smart grid. A possible application of smart grids is demand response, i.e., the ability to influence and control power demand to match it with fluctuating generation. We present a heuristic approach to coordinate large amounts of time-flexible loads in a smart grid with the aim of peak shaving with a focus on algorithmic efficiency. A practical evaluation shows that our approach scales to large instances and produces results that come close to optimality.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Notes

  1. 1.

    http://www.boost.org/doc/libs/1_64_0/libs/icl/doc/html/index.html.

References

  1. 1.

    Allerding F, Mauser I, Schmeck H (2014) Customizable energy management in smart buildings using evolutionary algorithms. Springer, Berlin, pp 153–164. doi:10.1007/978-3-662-45523-4_13

    Google Scholar 

  2. 2.

    Ashok S (2006) Peak-load management in steel plants. Appl Energy 83(5):413–424. doi:10.1016/j.apenergy.2005.05.002

    Article  Google Scholar 

  3. 3.

    Chuzhoy J, Guha S, Khanna S, Naor JS (2004) Machine minimization for scheduling jobs with interval constraints. In: 45th annual IEEE symposium on foundations of computer science, pp 81–90. doi:10.1109/FOCS.2004.38

  4. 4.

    Cieliebak M, Erlebach T, Hennecke F, Weber B, Widmayer P (2004) Scheduling with release times and deadlines on a minimum number of machines. Springer, Boston, pp 209–222. doi:10.1007/1-4020-8141-3_18

    Google Scholar 

  5. 5.

    Deckro RF, Hebert JE (1989) Resource constrained project crashing. Omega 17(1):69–79. doi:10.1016/0305-0483(89)90022-4

    Article  Google Scholar 

  6. 6.

    Earle R, Kahn EP, Macan E (2009) Measuring the capacity impacts of demand response. Electr J 22(6):47–58. doi:10.1016/j.tej.2009.05.014

    Article  Google Scholar 

  7. 7.

    Fang X, Misra S, Xue G, Yang D (2012) Smart grid the new and improved power grid: a survey. IEEE Commun Surv Tutor 14(4):944–980. doi:10.1109/SURV.2011.101911.00087

    Article  Google Scholar 

  8. 8.

    Gottwalt S, Grttner J, Schmeck H, Weinhardt C (2016) Modeling and valuation of residential demand flexibility for renewable energy integration. IEEE Trans Smart Grid PP(99):1–10. doi:10.1109/TSG.2016.2529424

    Google Scholar 

  9. 9.

    Guldemond TA, Hurink JL, Paulus JJ, Schutten JMJ (2008) Time-constrained project scheduling. J Sched 11(2):137–148. doi:10.1007/s10951-008-0059-7

    MathSciNet  Article  MATH  Google Scholar 

  10. 10.

    Kolter JZ, Johnson, MJ (2011) Redd: a public data set for energy disaggregation research. In: SustKDD

  11. 11.

    Li Y, Ng BL, Trayer M, Liu L (2012) Automated residential demand response: algorithmic implications of pricing models. IEEE Trans Smart Grid 3(4):1712–1721. doi:10.1109/TSG.2012.2218262

    Article  Google Scholar 

  12. 12.

    Mitra S, Grossmann IE, Pinto JM, Arora N (2012) Optimal production planning under time-sensitive electricity prices for continuous power-intensive processes. Comput Chem Eng 38:171–184. doi:10.1016/j.compchemeng.2011.09.019

    Article  Google Scholar 

  13. 13.

    Pedrasa MAA, Spooner TD, MacGill IF (2010) Coordinated scheduling of residential distributed energy resources to optimize smart home energy services. IEEE Trans Smart Grid 1(2):134–143. doi:10.1109/TSG.2010.2053053

    Article  Google Scholar 

  14. 14.

    Petersen MK, Hansen LH, Bendtsen J, Edlund K, Stoustrup J (2014) Heuristic optimization for the discrete virtual power plant dispatch problem. IEEE Trans Smart Grid 5(6):2910–2918. doi:10.1109/TSG.2014.2336261

    Article  Google Scholar 

  15. 15.

    Pritsker AAB, Waiters LJ, Wolfe PM (1969) Multiproject scheduling with limited resources: a zero-one programming approach. Manag Sci 16(1):93–108

  16. 16.

    Siano P (2014) Demand response and smart grids a survey. Renew Sustain Energy Rev 30:461–478. doi:10.1016/j.rser.2013.10.022

    Article  Google Scholar 

  17. 17.

    US Department of Energy (2006) Benefits of demand response in electricity markets and recommendations for achieving them

  18. 18.

    Yaw S, Mumey B, McDonald E, Lemke J (2014) Peak demand scheduling in the smart grid. In: 2014 IEEE international conference on smart grid communications (SmartGridComm), pp 770–775. doi:10.1109/SmartGridComm.2014.7007741

  19. 19.

    Zibelman A, Krapels EN (2008) Deployment of demand response as a real-time resource in organized markets. Electr J 21(5):51–56. doi:10.1016/j.tej.2008.05.011

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Lukas Barth.

Additional information

Lukas Barth’s work was supported by the German Research Foundation (DFG) as part of the Research Training Group GRK 2153: Energy Status Data—Informatics Methods for its Collection, Analysis and Exploitation.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Barth, L., Wagner, D. Exploiting flexibility in smart grids at scale. Comput Sci Res Dev 33, 185–191 (2018). https://doi.org/10.1007/s00450-017-0357-4

Download citation

Keywords

  • Smart grids
  • Demand response
  • Scheduling
  • Heuristics