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Control of Smart Grid Residential Buildings with Demand Response

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Chaos Modeling and Control Systems Design

Part of the book series: Studies in Computational Intelligence ((SCI,volume 581))

Abstract

The higher penetration of renewable energies into the electrical grid and the increasing power demand will transform the current grid model. The traditional production-oriented grid will be replaced by a more dynamic grid, known as the Smart Grid, where consumption will be adapted to the momentary available production. Getting flexibility in the demand side is a multidisciplinary challenge that is gaining the attention of both academia and industry. This chapter describes a residential building that can support the electrical grid providing flexibility (demand response) to a third party (aggregator) and discusses about computational intelligence techniques to be used in this scenario. For that purpose, a virtual power plant of a residential building is used to regulate the energy resources in the building in an optimal way and bring flexibility to the grid by aggregating demand response of households. The virtual power plant receives as inputs sensor data of the building and also external information from the electricity market, the customers, the aggregator and prediction models. The computational intelligence of the virtual power plant processes all these inputs to make decisions about the flexibility to provide to the grid and to control the electricity systems in the building using a model predictive control. The content of the chapter is supported by a description of a pilot study carried out in the city of Aarhus in Denmark, where a prototype of a virtual power plant will monitor and control a building with 159 apartments.

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Notes

  1. 1.

    The Federal Energy Regulatory Commission (FERC) estimated the contribution from existing United States demand response resources at about 5.8 per cent of 2008 summer peak demand. Moreover, FERC recently estimated nationwide achievable demand response potential at 138,000 MW (14 per cent of peak demand) by 2019.

  2. 2.

    Passive infra-red (PIR) sensors are used for motion detection.

  3. 3.

    The Pareto front is a curve that represents the optimal performance for a multi-objective optimisation with conflicting goals.

  4. 4.

    Computational fluid dynamics (CFD) is a branch of fluid mechanics that solve problems related with fluid flows using numerical methods and algorithms.

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Acknowledgements

The work in this document has been funded by the Danish Energy Agency project: Virtual Power Plant for Smart Grid Ready Buildings and Customers (no. 12019).

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Authors and Affiliations

  1. Aarhus University, Finlandsgade 22, 8200, Aarhus, Denmark

    Sergi Rotger-Griful & Rune Hylsberg Jacobsen

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  1. Sergi Rotger-Griful
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  2. Rune Hylsberg Jacobsen
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Correspondence to Sergi Rotger-Griful .

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Editors and Affiliations

  1. Faculty of Computers and Information, Benha University, Benha, Egypt

    Ahmad Taher Azar

  2. Research and Development Centre, Vel Tech University, Chennai, India

    Sundarapandian Vaidyanathan

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Rotger-Griful, S., Jacobsen, R.H. (2015). Control of Smart Grid Residential Buildings with Demand Response. In: Azar, A., Vaidyanathan, S. (eds) Chaos Modeling and Control Systems Design. Studies in Computational Intelligence, vol 581. Springer, Cham. https://doi.org/10.1007/978-3-319-13132-0_7

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  • DOI: https://doi.org/10.1007/978-3-319-13132-0_7

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