Hybrid Multi-ensemble Scheduling
Abstract
A steadily increasing pervasion of the electrical distribution grid with rather small renewable energy resources imposes fluctuating and hardly predictable feed-in, a partly reverse load flow and demands new predictive load planning strategies. For predictive scheduling with high penetration of renewable energy resources, agent-based approaches using classifier-based decoders for modeling individual flexibilities have shown good performance. On the other hand, such decoder-based methods are currently designed for single entities and not able to cope with ensembles of energy resources. Combining training sets sampled from individually modeled energy units, results in folded distributions with unfavorable properties for training a decoder. Nevertheless, this happens to be a quite frequent use case, e. g. when a hotel, a small business, a school or similar with an ensemble of co-generation, heat pump, solar power, and controllable consumers wants to take part in decentralized predictive scheduling. In this paper, we propose an extension to an established agent approach for scheduling individual single energy units by extending the agents’ decision routine with a covariance matrix adaption evolution strategy that is hybridized with decoders. In this way, locally managed ensembles of energy units can be included. We show the applicability of our approach by conducting several simulation studies.
Keywords
Predictive scheduling CMA-ES Multi-agent system Smart gridReferences
- 1.European Parliament & Council: Directive 2009/28/ec of 23 april 2009 on the promotion of the use of energy from renewable sources and amending and subsequently repealing directives 2001/77/ec and 2003/30/ecGoogle Scholar
- 2.Abarrategui, O., Marti, J., Gonzalez, A.: Constructing the active European power grid. In: Proceedings of WCPEE 2009, Cairo (2009)Google Scholar
- 3.Niee, A., Lehnhoff, S., Trschel, M., Uslar, M., Wissing, C., Appelrath, H.J., Sonnenschein, M.: Market-based self-organized provision of active power and ancillary services: an agent-based approach for smart distribution grids. In: Complexity in Engineering (COMPENG 2012), pp. 1–5, June 2012Google Scholar
- 4.Vinay Kumar, K., Balakrishna, R.: Smart grid: advanced metering infrastructure (AMI) & distribution management systems (DMS). Int. J. Comput. Sci. Eng. 3(11), 19–22 (2015)Google Scholar
- 5.Colak, I., Fulli, G., Sagiroglu, S., Yesilbudak, M., Covrig, C.F.: Smart grid projects in Europe: current status, maturity and future scenarios. Appl. Energy 152, 58–70 (2015)CrossRefGoogle Scholar
- 6.Awerbuch, S., Preston, A.M. (eds.): The Virtual Utility: Accounting, Technology & Competitive Aspects of the Emerging Industry. Topics in Regulatory Economics and Policy, vol. 26. Kluwer Academic Publishers, Heidelberg (1997)Google Scholar
- 7.Sonnenschein, M., Lünsdorf, O., Bremer, J., Tröschel, M.: Decentralized control of units in smart grids for the support of renewable energy supply. Environ. Impact Assess. Rev. (2014, in press)Google Scholar
- 8.Kamphuis, R., Warmer, C., Hommelberg, M., Kok, K.: Massive coordination of dispersed generation using powermatcher based software agents. In: 19th International Conference on Electricity Distribution, May 2007Google Scholar
- 9.Kok, K., Derzsi, Z., Gordijn, J., Hommelberg, M., Warmer, C., Kamphuis, R., Akkermans, H.: Agent-based electricity balancing with distributed energy resources, a multiperspective case study. In: Hawaii International Conference on System Sciences, p. 173 (2008)Google Scholar
- 10.Kamper, A., Esser, A.: Strategies for decentralised balancing power. In: Lewis, A., Mostaghim, S., Randall, M. (eds.) Biologically-Inspired Optimisation Methods: Parallel Algorithms, Systems and Applications. Studies in Computational Intelligence, vol. 210, pp. 261–289. Springer, Heidelberg (2009)CrossRefGoogle Scholar
- 11.Mihailescu, R.-C., Vasirani, M., Ossowski, S.: Dynamic coalition adaptation for efficient agent-based virtual power plants. In: Klügl, F., Ossowski, S. (eds.) MATES 2011. LNCS (LNAI), vol. 6973, pp. 101–112. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-24603-6_11CrossRefGoogle Scholar
- 12.Ramchurn, S.D., Vytelingum, P., Rogers, A., Jennings, N.R.: Agent-based control for decentralised demand side management in the smart grid. In: Sonenberg, L., Stone, P., Tumer, K., Yolum, P. (eds.) AAMAS, IFAAMAS, pp. 5–12 (2011)Google Scholar
- 13.Bremer, J., Rapp, B., Sonnenschein, M.: Support vector based encoding of distributed energy resources’ feasible load spaces. In: IEEE PES Conference on Innovative Smart Grid Technologies Europe, Chalmers Lindholmen, Gothenburg, Sweden (2010)Google Scholar
- 14.Bremer, J., Sonnenschein, M.: Constraint-handling for optimization with support vector surrogate models - a novel decoder approach. In: Filipe, J., Fred, A. (eds.) Proceedings of the 5th International Conference on Agents and Artificial Intelligence, ICAART 2013, Barcelona, Spain, vol. 2, pp. 91–105. SciTePress (2013)Google Scholar
- 15.Nieße, A., Sonnenschein, M.: A fully distributed continuous planning approach for decentralized energy units. In: Cunningham, D.W., Hofstedt, P., Meer, K., Schmitt, I. (eds.) Informatik 2015. GI-Edition - Lecture Notes in Informatics, 246 edn., pp. 151–165. Bonner Köllen Verlag, Bonn (2015).Google Scholar
- 16.Nieße, A., Beer, S., Bremer, J., Hinrichs, C., Lünsdorf, O., Sonnenschein, M.: Conjoint dynamic aggrgation and scheduling for dynamic virtual power plants. In: Ganzha, M., Maciaszek, L.A., Paprzycki, M. (eds.) Federated Conference on Computer Science and Information Systems - FedCSIS 2014, Warsaw, Poland, September 2014Google Scholar
- 17.Bremer, J., Sonnenschein, M.: Parallel tempering for constrained many criteria optimization in dynamic virtual power plants. In: 2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG), pp. 1–8, December 2014Google Scholar
- 18.Schiendorfer, A., Steghöfer, J.P., Reif, W.: Synthesised constraint models for distributed energy management. In: Ganzha, M., Maciaszek, L.A., Paprzycki, M. (eds.) Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, Warsaw, Poland, 7–10 September 2014, pp. 1529–1538 (2014)Google Scholar
- 19.Hinrichs, C.: Selbstorganisierte Einsatzplanung dezentraler Akteure im Smart Grid. Ph.D. thesis, Carl von Ossietzky Universitt Oldenburg (2014)Google Scholar
- 20.Bremer, J., Lehnhoff, S.: Decentralized coalition formation in agent-based smart grid applications. In: Bajo, J., et al. (eds.) PAAMS 2016. CCIS, vol. 616, pp. 343–355. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-39387-2_29Google Scholar
- 21.McArthur, S., Davidson, E., Catterson, V., Dimeas, A., Hatziargyriou, N., Ponci, F., Funabashi, T.: Multi-agent systems for power engineering applications - Part I: concepts, approaches, and technical challenges. IEEE Trans. Power Syst. 22(4), 1743–1752 (2007)CrossRefGoogle Scholar
- 22.Sonnenschein, M., Hinrichs, C., Nieße, A., Vogel, U.: Supporting renewable power supply through distributed coordination of energy resources. In: Hilty, L.M., Aebischer, B. (eds.) ICT Innovations for Sustainability. AISC, vol. 310, pp. 387–404. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-09228-7_23Google Scholar
- 23.Gieseke, F., Kramer, O.: Towards non-linear constraint estimation for expensive optimization. In: Esparcia-Alcázar, A.I. (ed.) EvoApplications 2013. LNCS, vol. 7835, pp. 459–468. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-37192-9_46CrossRefGoogle Scholar
- 24.Bremer, J., Sonnenschein, M.: Model-based integration of constrained search spaces into distributed planning of active power provision. Comput. Sci. Inf. Syst. 10(4), 1823–1854 (2013)CrossRefGoogle Scholar
- 25.Coello, C.A.C.: Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput. Methods Appl. Mech. Eng. 191(11–12), 1245–1287 (2002)MathSciNetCrossRefMATHGoogle Scholar
- 26.Hinrichs, C., Sonnenschein, M., Lehnhoff, S.: Evaluation of a self-organizing heuristic for interdependent distributed search spaces. In: Filipe, J., Fred, A.L.N. (eds.) International Conference on Agents and Artificial Intelligence (ICAART 2013), vol. 1, pp. 25–34. SciTePress (2013)Google Scholar
- 27.Hinrichs, C., Lehnhoff, S., Sonnenschein, M.: A decentralized heuristic for multiple-choice combinatorial optimization problems. In: Helber, S., et al. (eds.) Operations Research Proceedings 2012, pp. 297–302. Springer, Heidelberg (2014)CrossRefGoogle Scholar
- 28.Hinrichs, C., Bremer, J., Sonnenschein, M.: Distributed hybrid constraint handling in large scale virtual power plants. In: IEEE PES Conference on Innovative Smart Grid Technologies Europe (ISGT Europe 2013). IEEE Power & Energy Society (2013)Google Scholar
- 29.Nieße, A., Sonnenschein, M.: A fully distributed continuous planning approach for decentralized energy units. In: Cunningham, D.W., Hofstedt, P., Meer, K., Schmitt, I., eds.: Informatik 2015. GI-Edition - Lecture Notes in Informatics (LNI), vol. 246, pp. 151–165. Bonner Köllen Verlag, Bonn (2015)Google Scholar
- 30.Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization. Swarm Intell. 1(1), 33–57 (2007)CrossRefGoogle Scholar
- 31.Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39(3), 459–471 (2007)MathSciNetCrossRefMATHGoogle Scholar
- 32.Lust, T., Teghem, J.: The multiobjective multidimensional knapsack problem: a survey and a new approach. CoRR abs/1007.4063 (2010)Google Scholar
- 33.Watts, D., Strogatz, S.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440–442 (1998)CrossRefMATHGoogle Scholar
- 34.Bremer, J., Sonnenschein, M.: Sampling the search space of energy resources for self-organized, agent-based planning of active power provision. In: Page, B., Fleischer, A.G., Göbel, J., Wohlgemuth, V. (eds.) 27th International Conference on Environmental Informatics for Environmental Protection, EnviroInfo 2013, Shaker, pp. 214–222 (2013)Google Scholar
- 35.Hall, P.: The distribution of means for samples of size n drawn from a population in which the variate takes values between 0 and 1, all such values being equally probable. Biometrika 19(3/4), 240–245 (1927)CrossRefMATHGoogle Scholar
- 36.Ostermeier, A., Gawelczyk, A., Hansen, N.: A derandomized approach to self-adaptation of evolution strategies. Evol. Comput. 2(4), 369–380 (1994)CrossRefGoogle Scholar
- 37.Hansen, N.: The CMA evolution strategy: a comparing review. In: Lozano, J., Larranaga, P., Inza, I., Bengoetxea, E. (eds.) Towards a New Evolutionary Computation: Advances in the Estimation of Distribution Algorithms. Studies in Fuzziness and Soft Computing, vol. 192, pp. 75–102. Springer, Heidelberg (2006)CrossRefGoogle Scholar
- 38.Hansen, N.: The CMA evolution strategy: a tutorial. Technical report (2011)Google Scholar
- 39.Kramer, O., Barthelmes, A., Rudolph, G.: Surrogate constraint functions for CMA evolution strategies. In: Mertsching, B., Hund, M., Aziz, Z. (eds.) KI 2009. LNCS (LNAI), vol. 5803, pp. 169–176. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-04617-9_22CrossRefGoogle Scholar
- 40.Arnold, D.V., Hansen, N.: A (1+1)-CMA-ES for constrained optimisation. In: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, GECCO 2012, pp. 297–304. ACM, New York (2012)Google Scholar
- 41.Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evol. Comput. 9(2), 159–195 (2001)CrossRefGoogle Scholar
- 42.Bremer, J., Rapp, B., Sonnenschein, M.: Encoding distributed search spaces for virtual power plants. In: IEEE Symposium Series on Computational Intelligence 2011 (SSCI 2011), Paris, France, April 2011Google Scholar
- 43.Neugebauer, J., Kramer, O., Sonnenschein, M.: Classification cascades of overlapping feature ensembles for energy time series data. In: Woon, W.L., Aung, Z., Madnick, S. (eds.) DARE 2015. LNCS (LNAI), vol. 9518, pp. 76–93. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-27430-0_6CrossRefGoogle Scholar
- 44.Sonnenschein, M., Appelrath, H.J., Canders, W.R., Henke, M., Uslar, M., Beer, S., Bremer, J., Lünsdorf, O., Nieße, A., Psola, J.H., et al.: Decentralized provision of active power. In: Smart Nord - Final Report. Hartmann GmbH, Hannover (2015)Google Scholar