Abstract
Demand response is a promising approach to deal with the emerging power generation fluctuations introduced by the increasing amount of renewable energy sources fed into the grid. Consumers need to be able to adapt their energy consumption with respect to the given demand pattern and at the same time ensure that their adaptation (i.e., response) does not interfere with their various operational objectives. Finding, evaluating and verifying adaptation strategies which aim to be optimal w.r.t. multiple criteria is a challenging task and is currently mainly addressed by hand, heuristics or guided simulation. In this paper we carry out a case study of a demand response system with an energy adaptive data center on the consumer side for which we propose a formal model and perform a quantitative system analysis using probabilistic model checking. Our first contribution is a fine-grained formal model and the identification of significant properties and quantitative measures (e.g., expected energy consumption, average workload or total penalties for violating adaptation contracts) that are relevant for the data center as an adaptive consumer. The formal model can serve as a starting point for the application of different formal analysis methods. The second contribution is an evaluation of our approach using the prominent model checker PRISM. We report on the experimental results computing various functional properties and quantitative measures that yield important insights into the viability of given adaptation strategies and how to find close-to-optimal strategies.
The authors are supported by the DFG through the Collaborative Research Center SFB 912 – HAEC, the Excellence Initiative by the German Federal and State Governments (cluster of excellence cfAED and Institutional Strategy), the Research Training Groups QuantLA (GRK 1763) and RoSI (GRK 1907), and by the EU through the European Union’s Seventh Framework Programme FP7 in the context of the DC4Cities project (grant agreement No. 609304).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Baier, C., Daum, M., Dubslaff, C., Klein, J., Klüppelholz, S.: Energy-utility quantiles. In: Badger, J.M., Rozier, K.Y. (eds.) NFM 2014. LNCS, vol. 8430, pp. 285–299. Springer, Heidelberg (2014)
Baier, C., Klein, J., Klüppelholz, S., Märcker, S.: Computing conditional probabilities in Markovian models efficiently. In: Ábrahám, E., Havelund, K. (eds.) TACAS 2014 (ETAPS). LNCS, vol. 8413, pp. 515–530. Springer, Heidelberg (2014)
Baier, C., Klein, J., Klüppelholz, S., Wunderlich, S.: Weight monitoring with linear temporal logic: complexity and decidability. In: Proceedings of the Joint Meeting of the Twenty-Third EACSL Annual Conference on Computer Science Logic (CSL) and the Twenty-Ninth Annual ACM/IEEE Symposium on Logic in Computer Science (LICS), pp. 11:1–11:10. ACM (2014)
Baier, C., Klüppelholz, S., de Meer, H., Niedermeier, F., Wunderlich, S.: Greener bits: formal analysis of demand response. Technical report, TU Dresden (2016). http://www.tcs.inf.tu-dresden.de/ALGI/PUB/ATVA16/
Balijepalli, V., Pradhan, V., Khaparde, S., Shereef, R.: Review of demand response under smart grid paradigm. In: 2011 IEEE PES on Innovative Smart Grid Technologies-India (ISGT India), pp. 236–243. IEEE (2011)
Basmadjian, R., Lovasz, G., Beck, M., de Meer, H., Hesselbach-Serra, X., Botero, J., Klingert, S., Ortega, M.P., Lopez, J., Stam, A., van Krevelen, R., Girolamo, M.D.: A generic architecture for demand response: the ALL4Green approach. In: Proceedings of the 3rd International Conference on Cloud and Green Computing (CGC), pp. 464–471. IEEE (2013)
Basmadjian, R., Mueller, L., de Meer, H.: Data centres’ power profile selecting policies for demand response: insights of green supply demand agreement. Ad Hoc Netw. 25, 581–594 (2015)
Boudec, J.L., Tomozei, D.-C.: Stability of a stochastic model for demand-response. Stoch. Syst. 3(1), 11–37 (2013)
Chan, S., Schweppe, F.: A generation reallocation and load shedding algorithm. IEEE Trans. Power Appar. Syst. 1(PAS–98), 26–34 (1979)
Chen, H., Coskun, A.K., Caramanis, M.C.: Real-time power control of data centers for providing regulation service. In: Proceedings of the 52nd IEEE Conference on Decision and Control, pp. 4314–4321 (2013)
Chen, N., Ren, X., Ren, S., Wierman, A.: Greening multi-tenant data center demand response. SIGMETRICS Perform. Eval. Rev. 43(2), 36–38 (2015)
Chrszon, P., Dubslaff, C., Klüppelholz, S., Baier, C.: Family-based modeling and analysis for probabilistic systems – featuring ProFeat. In: Stevens, P., Wąsowski, A. (eds.) FASE 2016. LNCS, vol. 9633, pp. 287–304. Springer, Heidelberg (2016). doi:10.1007/978-3-662-49665-7_17
David, A., Grunnet, J.D., Jessen, J.J., Larsen, K.G., Rasmussen, J.I.: Application of model-checking technology to controller synthesis. In: Aichernig, B.K., de Boer, F.S., Bonsangue, M.M. (eds.) Formal Methods for Components and Objects. LNCS, vol. 6957, pp. 336–351. Springer, Heidelberg (2011)
Forejt, V., Kwiatkowska, M., Norman, G., Parker, D.: Automated verification techniques for probabilistic systems. In: Bernardo, M., Issarny, V. (eds.) SFM 2011. LNCS, vol. 6659, pp. 53–113. Springer, Heidelberg (2011)
Gandhi, A., Gupta, V., Harchol-Balter, M., Kozuch, M.: Optimality analysis of energy-performance trade-off for server farm management. Perform. Eval. 67(11), 1155–1171 (2010)
Huang, G.M., Nair, N.: An OPF based algorithm to evaluate load curtailment incorporating voltage stability margin criterion. In: Proceedings of NAPS Conference (2001)
Katoen, J.-P.: Model checking: one can do much more than you think!. In: Arbab, F., Sirjani, M. (eds.) FSEN 2011. LNCS, vol. 7141, pp. 1–14. Springer, Heidelberg (2012)
Kaur, H., Brar, Y.S., Randhawa, J.S.: Optimal power flow using power world simulator. In: Proceedings of the 2010 IEEE Electric Power and Energy Conference (EPEC), pp. 1–6 (2010)
Klein, J., Baier, C., Chrszon, P., Daum, M., Dubslaff, C., Klüppelholz, S., Märcker, S., Müller, D.: Advances in symbolic probabilistic model checking with PRISM. In: Chechik, M., Raskin, J.-F. (eds.) TACAS 2016. LNCS, vol. 9636, pp. 349–366. Springer, Heidelberg (2016). doi:10.1007/978-3-662-49674-9_20
Klingert, S., Niedermeier, F., Dupont, C., Giuliani, G., Schulze, T., de Meer, H.: Renewable energy-aware data centre operations for smart cities the DC4Cities approach. In: Proceedings of the International Conference on Smart Cities and Green ICT Systems (SMARTGREENS), pp. 1–9 (2015)
Kwiatkowska, M., Norman, G., Parker, D.: PRISM 4.0: verification of probabilistic real-time systems. In: Gopalakrishnan, G., Qadeer, S. (eds.) CAV 2011. LNCS, vol. 6806, pp. 585–591. Springer, Heidelberg (2011)
Liu, Z., Liu, I., Low, S., Wierman, A.: Pricing data center demand response. In: Proceedings of the 2014 ACM International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS), pp. 111–123 (2014)
Medicherla, T., Billinton, R., Sachdev, M.: Generation rescheduling and load shedding to alleviate line overloads-analysis. IEEE Trans. Power Appar. Syst. PAS–98(6), 1876–1884 (1979)
Meidani, H., Ghanem, R.: Multiscale Markov models with random transitions for energy demand management. Energy Build. 61, 267–274 (2013)
Niedermeier, F., Duschl, W., Möller, T., de Meer, H.: Increasing data centre renewable power share via intelligent smart city power control. In: Proceedings of the 2015 ACM International Conference on Future Energy Systems, pp. 241–246 (2015)
Norman, G., Parker, D., Kwiatkowska, M., Shukla, S., Gupta, R.: Using probabilistic model checking for dynamic power management. Formal Aspects Comput. 17(2), 160–176 (2005)
Puterman, M.: Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York (1994)
Qureshi, A.: Power-demand routing in massive geo-distributed systems. Ph.D. thesis, Massachusetts Institute of Technology (2010)
Siano, P.: Demand response and smart grids—a survey. Renew. Sustain. Energy Rev. 30, 461–478 (2014)
Zhou, Z., Liu, F., Li, B., Li, B., Jin, H., Zou, R., Liu, Z.: Fuel cell generation in geo-distributed cloud services: a quantitative study. In: Proceedings of the 34th International Conference on Distributed Computing Systems (ICDCS), pp. 52–61 (2014)
Zhou, Z., Liu, F., Li, Z.: Pricing bilateral electricity trade between smart grids and hybrid green datacenters. In: Proceedings of the 2015 ACM International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS), pp. 443–444. ACM (2015)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Baier, C., Klüppelholz, S., de Meer, H., Niedermeier, F., Wunderlich, S. (2016). Greener Bits: Formal Analysis of Demand Response. In: Artho, C., Legay, A., Peled, D. (eds) Automated Technology for Verification and Analysis. ATVA 2016. Lecture Notes in Computer Science(), vol 9938. Springer, Cham. https://doi.org/10.1007/978-3-319-46520-3_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-46520-3_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46519-7
Online ISBN: 978-3-319-46520-3
eBook Packages: Computer ScienceComputer Science (R0)