Abstract
Decentralized renewable energy sources become more and more common. This leads to stability problems in power grids. Conventional energy sources are easy to control. In contrast, wind and solar power are much more difficult to forecast. Forecasts are only possible short term and are more imprecise. Producers and consumers of energy can try to help reducing stability problems. Contributions towards a decision support system are proposed and recommend how to alter the behavior of producers and consumers. On the producer side centrally controlled heat and power plants are able to shift load in a virtual power plant. The plant operator offers a load curve based on forecasts. The centrally controlled heat and power plants help to mitigate the effect of revised forecasts. An incentive based control on the consumer side is also proposed. Smart appliances react to pricing information. They alter their execution window towards the cheapest time slot, if possible. The exact behavior of appliances in the expected field experiment is still partially unknown. It is necessary to simulate the behavior of these appliances and to train an artificial neural network. The artificial neural network allows computing the pricing signal leading to a desired load shift.
Similar content being viewed by others
References
Appelrath HJ, Chamoni P (2007) Veränderungen in der Energiewirtschaft – Herausforderungen für die IT. WIRTSCHAFTSINFORMATIK 49(5):329–330
Brandt P (2007) IT in der Energiewirtschaft. WIRTSCHAFTSINFORMATIK 49(5):380–385
Breitner MH (2003) Nichtlinerare, multivariate Approximation mit Perzeptrons und anderen Funktionen auf verschiedenen Hochleistungsrechnern. Akademische Verlagsgesellschaft, Berlin
Bundesnetzagentur (2011) “Smart Grid” und “Smart Market” – Eckpunktepapier der Bundesnetzagentur zu den Aspekten des sich verändernden Energieversorgungssystems, Bonn
Eßer A, Franke M, Kamper A, Möst D (2007) Future power markets – impacts of consumer response and dynamic retail prices on electricity markets. WIRTSCHAFTSINFORMATIK 49(5):335–341
Fluhr J, Ahlert KH, Weinhardt C (2010) A stochastic model for simulating the availability of electric vehicles for services to the power grid. In: Proc 43rd Hawaii international conference on system sciences (HICSS), Honolulu
Goutard E (2010) Renewable energy resources in energy management systems. In: Proc innovative smart grid technologies conference Europe (ISGT Europe), Gothenburg, IEEE
Hauttekeete L, Stragier J, Haerick W, De Marez L (2010) Smart, smarter, smartest… the consumer meets the smart electrical grid. In: Proc 9th conference on telecommunications internet and media techno economics (CTTE), Ghent
Haykin S (2009) Neural networks and learning machines, 3rd edn. Pearson, Upper Saddle River
Hevner AR, March ST, Park J, Ram S (2004) Design science in information systems research. MIS Quarterly 28(1):75–105
Köpp C, von Mettenheim HJ, Klages M, Breitner MH (2010) Analysis of electrical load balancing by simulation and neural network forecast. In: Proc international conference operations research, München, pp 519–524
Molderink A, Bakker V, Bosman MGC, Hurink J, Smit GJM (2010) Management and control of domestic smart grid technology. IEEE Transactions on Smart Grid 1(2):109–119
Nwulu NI, Fahrioglu M (2011) A neural network model for optimal demand management contract design. In: Proc environment and electrical engineering, Rome, IEEE
Sonnenschein M, Stadler M, Rapp B, Bremer J, Brunhorn S (2006) A modelling and simulation environment for real-time pricing scenarios in energy markets. In: Tochtermann K, Scharl A (eds) Proc 20th international conference on informatics for environmental protection, Graz, pp 153–160
Stadler M, Krause W, Sonnenschein M, Vogel U (2009) Modelling and evaluation of control schemes for enhancing load shift of electricity demand for cooling devices. Environmental Modelling & Software 24(2):285–295
Tröschel M, Appelrath HJ (2009) Towards reactive scheduling for large-scale virtual power plants. In: Braubach L, van der Hoek W, Petta P, Pokahr A (eds) Proc multiagent system technologies 7th German conference (MATES 2009), Hamburg, pp 141–152
Tröschel M, Lünsdorf O (2010) Conjoint generation management and load adaption for an optimized power grid utilization. In: Proc power and energy society general meeting, Minneapolis
von Mettenheim HJ, Breitner MH (2010) Robust decision support systems with matrix forecasts and shared layer perceptrons for finance and other applications. In: Proc ICIS 2010, St Louis, Paper 83
Wedde HF, Lehnhoff S, Handschin E, Krause O (2007) Dezentrale vernetzte Energiebewirtschaftung (DEZENT) im Netz der Zukunft. WIRTSCHAFTSINFORMATIK 49(5):361–369
Author information
Authors and Affiliations
Corresponding author
Additional information
Accepted after three revisions by Prof. Dr. Hans Ulrich Buhl.
This article is also available in German in print and via http://www.wirtschaftsinformatik.de: Köpp C, von Mettenheim H-J, Breitner MH (2012) Lastmanagement in Stromnetzen. Beiträge für ein Entscheidungsunterstützungssytem für Portfoliobetreiber. WIRTSCHAFTSINFORMATIK. doi: 10.1007/s11576-012-0348-9.
Rights and permissions
About this article
Cite this article
Köpp, C., von Mettenheim, HJ. & Breitner, M.H. Load Management in Power Grids. Bus Inf Syst Eng 5, 35–44 (2013). https://doi.org/10.1007/s12599-012-0246-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12599-012-0246-0