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Optimization Models and Methods for Demand-Side Management of Residential Users: A Survey

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  • Antimo Barbato

    (Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci, 32, Milano 20133, Italy)

  • Antonio Capone

    (Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci, 32, Milano 20133, Italy)

Abstract

The residential sector is currently one of the major contributors to the global energy balance. However, the energy demand of residential users has been so far largely uncontrollable and inelastic with respect to the power grid conditions. With the massive introduction of renewable energy sources and the large variations in energy flows, also the residential sector is required to provide some flexibility in energy use so as to contribute to the stability and efficiency of the electric system. To address this issue, demand management mechanisms can be used to optimally manage the energy resources of customers and their energy demand profiles. A very promising technique is represented by demand-side management (DSM), which consists in a proactive method aimed at making users energy-efficient in the long term. In this paper, we survey the most relevant studies on optimization methods for DSM of residential consumers. Specifically, we review the related literature according to three axes defining contrasting characteristics of the schemes proposed: DSM for individual users versus DSM for cooperative consumers, deterministic DSM versus stochastic DSM and day-ahead DSM versus real-time DSM. Based on this classification, we provide a big picture of the key features of different approaches and techniques and discuss future research directions.

Suggested Citation

  • Antimo Barbato & Antonio Capone, 2014. "Optimization Models and Methods for Demand-Side Management of Residential Users: A Survey," Energies, MDPI, vol. 7(9), pages 1-38, September.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:9:p:5787-5824:d:39951
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    References listed on IDEAS

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    1. Dimitris Bertsimas & Melvyn Sim, 2004. "The Price of Robustness," Operations Research, INFORMS, vol. 52(1), pages 35-53, February.
    2. Clastres, C. & Ha Pham, T.T. & Wurtz, F. & Bacha, S., 2010. "Ancillary services and optimal household energy management with photovoltaic production," Energy, Elsevier, vol. 35(1), pages 55-64.
    3. Considine, Timothy J., 2000. "The impacts of weather variations on energy demand and carbon emissions," Resource and Energy Economics, Elsevier, vol. 22(4), pages 295-314, October.
    4. E. L. Lawler & D. E. Wood, 1966. "Branch-and-Bound Methods: A Survey," Operations Research, INFORMS, vol. 14(4), pages 699-719, August.
    5. Ortega, Margarita & del Río, Pablo & Montero, Eduardo A., 2013. "Assessing the benefits and costs of renewable electricity. The Spanish case," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 294-304.
    6. Richards, Garrett & Noble, Bram & Belcher, Ken, 2012. "Barriers to renewable energy development: A case study of large-scale wind energy in Saskatchewan, Canada," Energy Policy, Elsevier, vol. 42(C), pages 691-698.
    7. Global Energy Assessment Writing Team,, 2012. "Global Energy Assessment," Cambridge Books, Cambridge University Press, number 9780521182935.
    8. Pfeiffer, Birte & Mulder, Peter, 2013. "Explaining the diffusion of renewable energy technology in developing countries," Energy Economics, Elsevier, vol. 40(C), pages 285-296.
    9. Hao Liang & Weihua Zhuang, 2014. "Stochastic Modeling and Optimization in a Microgrid: A Survey," Energies, MDPI, vol. 7(4), pages 1-24, March.
    10. Ron Shamir, 1987. "The Efficiency of the Simplex Method: A Survey," Management Science, INFORMS, vol. 33(3), pages 301-334, March.
    11. Voyant, Cyril & Muselli, Marc & Paoli, Christophe & Nivet, Marie-Laure, 2011. "Optimization of an artificial neural network dedicated to the multivariate forecasting of daily global radiation," Energy, Elsevier, vol. 36(1), pages 348-359.
    12. John M. Mulvey & Andrzej Ruszczyński, 1995. "A New Scenario Decomposition Method for Large-Scale Stochastic Optimization," Operations Research, INFORMS, vol. 43(3), pages 477-490, June.
    13. Fernandez-Jimenez, L. Alfredo & Muñoz-Jimenez, Andrés & Falces, Alberto & Mendoza-Villena, Montserrat & Garcia-Garrido, Eduardo & Lara-Santillan, Pedro M. & Zorzano-Alba, Enrique & Zorzano-Santamaria,, 2012. "Short-term power forecasting system for photovoltaic plants," Renewable Energy, Elsevier, vol. 44(C), pages 311-317.
    14. Strbac, Goran, 2008. "Demand side management: Benefits and challenges," Energy Policy, Elsevier, vol. 36(12), pages 4419-4426, December.
    15. Yu, Chian-Son & Li, Han-Lin, 2000. "A robust optimization model for stochastic logistic problems," International Journal of Production Economics, Elsevier, vol. 64(1-3), pages 385-397, March.
    16. Global Energy Assessment Writing Team,, 2012. "Global Energy Assessment," Cambridge Books, Cambridge University Press, number 9781107005198.
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