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An event driven Smart Home Controller enabling consumer economic saving and automated Demand Side Management

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  • Di Giorgio, Alessandro
  • Pimpinella, Laura

Abstract

This paper proposes the design of a Smart Home Controller strategy providing efficient management of electric energy in a domestic environment. The problem is formalized as an event driven binary linear programming problem, the output of which specifies the best time to run of smart household appliances, under a virtual power threshold constraint, taking into account the real power threshold and the forecast of consumption from not plannable loads. The optimization is performed each time the system is triggered by proper events, in order to tailor the controller action to the real life dynamics of an household. This problem formulation allows to analyze relevant scenarios from consumer and energy retailer point of view: here overload management, optimization of economic saving in case of Time of Use Tariff and Demand Side Management have been discussed and simulated. Simulations have been performed on relevant test cases, based on real load profiles provided by the smart appliance manufacturer Electrolux S.p.A. and on energy tariffs suggested by the energy retailer Edison. Results provide a proof of concept about the consumers benefits coming from the use of local energy management systems and the relevance of automated Demand Side Management for the general target of efficient and cost effective operation of electric networks.

Suggested Citation

  • Di Giorgio, Alessandro & Pimpinella, Laura, 2012. "An event driven Smart Home Controller enabling consumer economic saving and automated Demand Side Management," Applied Energy, Elsevier, vol. 96(C), pages 92-103.
    • Handle: RePEc:eee:appene:v:96:y:2012:i:c:p:92-103
    DOI: 10.1016/j.apenergy.2012.02.024
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    References listed on IDEAS

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    1. Lienert, Martin, 2008. "Leistungsvorhaltung auf Regelmaerkten. Excel Add-in, Beschreibung und Anleitung," EWI Working Papers 2008-3, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    2. van Staden, Adam Jacobus & Zhang, Jiangfeng & Xia, Xiaohua, 2011. "A model predictive control strategy for load shifting in a water pumping scheme with maximum demand charges," Applied Energy, Elsevier, vol. 88(12), pages 4785-4794.
    3. Middelberg, Arno & Zhang, Jiangfeng & Xia, Xiaohua, 2009. "An optimal control model for load shifting - With application in the energy management of a colliery," Applied Energy, Elsevier, vol. 86(7-8), pages 1266-1273, July.
    4. Ashok, S. & Banerjee, R., 2000. "Load-management applications for the industrial sector," Applied Energy, Elsevier, vol. 66(2), pages 105-111, June.
    5. Matallanas, E. & Castillo-Cagigal, M. & Gutiérrez, A. & Monasterio-Huelin, F. & Caamaño-Martín, E. & Masa, D. & Jiménez-Leube, J., 2012. "Neural network controller for Active Demand-Side Management with PV energy in the residential sector," Applied Energy, Elsevier, vol. 91(1), pages 90-97.
    6. Berg, Sandford V. & Savvides, Andreas, 1983. "The theory of maximum kW demand charges for electricity," Energy Economics, Elsevier, vol. 5(4), pages 258-266, October.
    7. Newsham, Guy R. & Bowker, Brent G., 2010. "The effect of utility time-varying pricing and load control strategies on residential summer peak electricity use: A review," Energy Policy, Elsevier, vol. 38(7), pages 3289-3296, July.
    8. Ashok, S., 2006. "Peak-load management in steel plants," Applied Energy, Elsevier, vol. 83(5), pages 413-424, May.
    9. E. L. Lawler & D. E. Wood, 1966. "Branch-and-Bound Methods: A Survey," Operations Research, INFORMS, vol. 14(4), pages 699-719, August.
    10. Moura, Pedro S. & de Almeida, Aníbal T., 2010. "The role of demand-side management in the grid integration of wind power," Applied Energy, Elsevier, vol. 87(8), pages 2581-2588, August.
    11. Gottwalt, Sebastian & Ketter, Wolfgang & Block, Carsten & Collins, John & Weinhardt, Christof, 2011. "Demand side management—A simulation of household behavior under variable prices," Energy Policy, Elsevier, vol. 39(12), pages 8163-8174.
    12. Paulus, Moritz & Borggrefe, Frieder, 2011. "The potential of demand-side management in energy-intensive industries for electricity markets in Germany," Applied Energy, Elsevier, vol. 88(2), pages 432-441, February.
    13. George L. Nemhauser, 1994. "The Age of Optimization: Solving Large-Scale Real-World Problems," Operations Research, INFORMS, vol. 42(1), pages 5-13, February.
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