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.
Notes
References
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
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
Ashok S (2006) Peak-load management in steel plants. Appl Energy 83(5):413–424. doi:10.1016/j.apenergy.2005.05.002
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
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
Denholm P, Ela E, Kirby B, Milligan M (2010) The role of energy storage with renewable electricity generation. Technical report
Du P, Lu N (2011) Appliance commitment for household load scheduling. IEEE Trans Smart Grid 2(2):411–419
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
Fink J, Hurink JL, Molderink A (2014) Mathematical modelling of devices and flows in energy systems. Technical report
Gärttner J (2016) Group formation in smart grids: Designing demand response portfolios. Ph.D. thesis, Dissertation, Karlsruher Institut für Technologie (KIT)
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
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
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
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
Halvorsen B, Larsen BM (2001) The flexibility of household electricity demand over time. Resource Energy Econ 23(1):1–18
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
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
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
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
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
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
Palensky P, Dietrich D (2011) Demand side management: demand response, intelligent energy systems, and smart loads. IEEE Trans Ind Inform 7(3):381–388
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
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
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
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
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
Schleicher-Tappeser R (2012) How renewables will change electricity markets in the next five years. Energy policy 48:64–75
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
Setlhaolo D, Xia X, Zhang J (2014) Optimal scheduling of household appliances for demand response. Electr Power Syst Res 116:24–28
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
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
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
Strbac G (2008) Demand side management: benefits and challenges. Energy Policy 36(12):4419–4426
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
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
Acknowledgements
We thank one anonymous reviewer for his extraordinarily constructive comments which helped us to improve the manuscript.
Author information
Authors and Affiliations
Corresponding author
Additional information
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”.
Rights and permissions
About this article
Cite this article
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
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00450-017-0343-x