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A comprehensive modelling framework for demand side flexibility in smart grids

Computer Science - Research and Development

Abstract

The increasing share of renewable energy generation in the electricity system comes with significant challenges, such as the volatility of renewable energy sources. To tackle those challenges, demand side management is a frequently mentioned remedy. However, measures of demand side management need a high level of flexibility to be successful. Although extensive research exists that describes, models and optimises various processes with flexible electrical demands, there is no unified notation. Additionally, most descriptions are very process-specific and cannot be generalised. In this paper, we develop a comprehensive modelling framework to mathematically describe demand side flexibility in smart grids while integrating a majority of constraints from different existing models. We provide a universally applicable modelling framework for demand side flexibility and evaluate its practicality by looking at how well Mixed-Integer Linear Program solvers are able to optimise the resulting models, if applied to artificially generated instances. From the evaluation, we derive that our model improves the performance of previous models while integrating additional flexibility characteristics.

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Notes

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References

  1. Alizadeh M, Scaglione A, Applebaum A, Kesidis G, Levitt K (2015) Reduced-order load models for large populations of flexible appliances. IEEE Trans Power Syst 30(4):1758–1774

    Article  Google Scholar 

  2. Allerding F, Premm M, Shukla PK, Schmeck H (2012) Electrical load management in smart homes using evolutionary algorithms. In: Hao JK, Middendorf M (eds) Lecture notes in computer science. Springer, Berlin, pp 99–110. doi:10.1007/978-3-642-29124-1_9

    Google Scholar 

  3. 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 

  4. Ashok S, Banerjee R (2000) Load-management applications for the industrial sector. Appl Energy 66(2):105–111. doi:10.1016/S0306-2619(99)00125-7

    Article  Google Scholar 

  5. Castro P, Matos H, Barbosa-Póvoa A (2002) Dynamic modelling and scheduling of an industrial batch system. Comput Chem Eng 26(4–5):671–686. doi:10.1016/S0098-1354(01)00792-X

    Article  Google Scholar 

  6. Denholm P, Ela E, Kirby B, Milligan M (2010) The role of energy storage with renewable electricity generation. Technical report

  7. Du P, Lu N (2011) Appliance commitment for household load scheduling. IEEE Trans Smart Grid 2(2):411–419

    Article  Google Scholar 

  8. Fehrenbach D, Merkel E, McKenna R, Karl U, Fichtner W (2014) On the economic potential for electric load management in the german residential heating sector-an optimising energy system model approach. Energy 71:263–276

    Article  Google Scholar 

  9. Fink J, Hurink JL, Molderink A (2014) Mathematical modelling of devices and flows in energy systems. Technical report

  10. Gärttner J (2016) Group formation in smart grids: Designing demand response portfolios. Ph.D. thesis, Dissertation, Karlsruher Institut für Technologie (KIT)

  11. Gärttner J, Flath CM, Weinhardt C (2016) Load shifting, interrupting or both? Customer portfolio composition in demand side management. In: Fonseca R, Weber GW, Telhada J (eds) Computational management science. Lecture notes in economics and mathematical systems, vol 682. Springer, Cham, pp 9–15. doi:10.1007/978-3-319-20430-7_2

  12. Goebel C, Jacobsen HA, Razo V, Doblander C, Rivera J, Ilg J, Flath C, Schmeck H, Weinhardt C, Pathmaperuma D, Appelrath HJ, Sonnenschein M, Lehnhoff S, Kramer O, Staake T, Fleisch E, Neumann D, Strüker J, Erek K, Zarnekow R, Ziekow H, Lässig J (2014) Energy informatics. Bus Inf Syst Eng 6(1):25–31

    Article  Google Scholar 

  13. Gottwalt S, Ketter W, Block C, Collins J, Weinhardt C (2011) Demand side management-a simulation of household behavior under variable prices. Energy Policy 39(12):8163–8174

    Article  Google Scholar 

  14. Gottwalt S, Gärttner J, Schmeck H, Weinhardt C (2016) Modeling and valuation of residential demand flexibility for renewable energy integration. In: IEEE transactions on smart grid, vol PP. IEEE, pp 1–10. doi:10.1109/TSG.2016.2529424

  15. Halvorsen B, Larsen BM (2001) The flexibility of household electricity demand over time. Resource Energy Econ 23(1):1–18

    Article  Google Scholar 

  16. He X, Keyaerts N, Azevedo I, Meeus L, Hancher L, Glachant JM (2013) How to engage consumers in demand response: a contract perspective. Util Policy 27:108–122. doi:10.1016/j.jup.2013.10.001

    Article  Google Scholar 

  17. Luo Z, Kumar R, Sottile J, Yingling JC (1998) An milp formulation for load-side demand control. Electr Mach Power Syst 26(9):935–949. doi:10.1080/07313569808955868

    Article  Google Scholar 

  18. Meindl B, Templ M (2012) Analysis of commercial and free and open source solvers for linear optimization problems. Eurostat and Statistics Netherlands within the project ESSnet on common tools and harmonised methodology for SDC in the ESS

  19. 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 

  20. Moon JY, Park J (2014) Smart production scheduling with time-dependent and machine-dependent electricity cost by considering distributed energy resources and energy storage. Int J Prod Res 52(13):3922–3939. doi:10.1080/00207543.2013.860251

    Article  Google Scholar 

  21. Oudalov A, Cherkaoui R, Beguin A (2007) Sizing and optimal operation of battery energy storage system for peak shaving application. In: 2007 IEEE Power Tech, pp 621–625. doi:10.1109/PCT.2007.4538388

  22. Palensky P, Dietrich D (2011) Demand side management: demand response, intelligent energy systems, and smart loads. IEEE Trans Ind Inform 7(3):381–388

    Article  Google Scholar 

  23. Paulus M, Borggrefe F (2011) The potential of demand-side management in energy-intensive industries for electricity markets in germany. Appl Energy 88(2):432–441

    Article  Google Scholar 

  24. Petersen MK, Hansen LH, Bendtsen J, Edlund K, Stoustrup J (2013) A taxonomy for modeling flexibility and a computationally efficient algorithm for dispatch in smart grids. In: 2013 American control conference (ACC), pp 1150–1156. doi:10.1109/ACC.2013.6579991

  25. 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

    Article  Google Scholar 

  26. Qureshi FA, Gorecki TT, Jones CN (2014) Model predictive control for market-based demand response participation. IFAC Proc Vol 47(3):11,153–11,158

    Article  Google Scholar 

  27. Schilling G, Pantelides CC (1996) A simple continuous-time process scheduling formulation and a novel solution algorithm. Comput Chem Eng 20:S1221–S1226. doi:10.1016/0098-1354(96)00211-6

    Article  Google Scholar 

  28. Schleicher-Tappeser R (2012) How renewables will change electricity markets in the next five years. Energy policy 48:64–75

    Article  Google Scholar 

  29. Scott P, Thiébaux S, Van Den Briel M, Van Hentenryck P (2013) Residential demand response under uncertainty. In: International conference on principles and practice of constraint programming. Springer, pp 645–660

  30. Setlhaolo D, Xia X, Zhang J (2014) Optimal scheduling of household appliances for demand response. Electr Power Syst Res 116:24–28

    Article  Google Scholar 

  31. Soares A, Gomes Á, Antunes CH (2014) Categorization of residential electricity consumption as a basis for the assessment of the impacts of demand response actions. Renew Sustain Energy Rev 30:490–503

    Article  Google Scholar 

  32. Sou KC, Weimer J, Sandberg H, Johansson KH (2011) Scheduling smart home appliances using mixed integer linear programming. In: 2011 50th IEEE conference on decision and control and european control conference. IEEE, Piscataway, NJ, pp 5144–5149

  33. Steurer M, Miller M, Fahl U, Hufendiek K (2015) Enabling demand side integration–assessment of appropriate information and communication technology infrastructures, their costs and possible impacts on the electricity system. SmartER Europe

  34. Strbac G (2008) Demand side management: benefits and challenges. Energy Policy 36(12):4419–4426

    Article  Google Scholar 

  35. Ströhle P, Gerding EH, de Weerdt MM, Stein S, Robu V (2014) Online mechanism design for scheduling non-preemptive jobs under uncertain supply and demand. In: Proceedings of the 2014 AAMAS, International foundation for autonomous agents and multiagent systems, pp 437–444

  36. Weidlich A, Vogt H, Krauss W, Spiess P, Jawurek M, Johns M, Karnouskos S (2012) Decentralized intelligence in energy efficient power systems. In: Sorokin A, Rebennack S, Pardalos P, Iliadis N, Pereira M (eds) Handbook of networks in power systems I. Energy systems. Springer, Berlin, Heidelberg, pp 467–486. doi:10.1007/978-3-642-23193-3_18

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Acknowledgements

We thank one anonymous reviewer for his extraordinarily constructive comments which helped us to improve the manuscript.

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Correspondence to Nicole Ludwig.

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This work was partly funded by the German Research Foundation (DFG) Research Training Group 2153 “Energy Status Data—Informatics Methods for its Collection, Analysis and Exploitation”.

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Barth, L., Ludwig, N., Mengelkamp, E. et al. A comprehensive modelling framework for demand side flexibility in smart grids. Comput Sci Res Dev 33, 13–23 (2018). https://doi.org/10.1007/s00450-017-0343-x

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  • DOI: https://doi.org/10.1007/s00450-017-0343-x

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