IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v7y2014i9p5787-5824d39951.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/7/9/5787/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/7/9/5787/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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, September.
    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, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ratanakuakangwan, Sudlop & Morita, Hiroshi, 2021. "Hybrid stochastic robust optimization and robust optimization for energy planning – A social impact-constrained case study," Applied Energy, Elsevier, vol. 298(C).
    2. Jabbarzadeh, Armin & Fahimnia, Behnam & Seuring, Stefan, 2014. "Dynamic supply chain network design for the supply of blood in disasters: A robust model with real world application," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 70(C), pages 225-244.
    3. Yan, Shangyao & Tang, Ching-Hui, 2009. "Inter-city bus scheduling under variable market share and uncertain market demands," Omega, Elsevier, vol. 37(1), pages 178-192, February.
    4. Mohammaddust, Faeghe & Rezapour, Shabnam & Farahani, Reza Zanjirani & Mofidfar, Mohammad & Hill, Alex, 2017. "Developing lean and responsive supply chains: A robust model for alternative risk mitigation strategies in supply chain designs," International Journal of Production Economics, Elsevier, vol. 183(PC), pages 632-653.
    5. Anders Arvesen & Steve Völler & Christine Roxanne Hung & Volker Krey & Magnus Korpås & Anders Hammer Strømman, 2021. "Emissions of electric vehicle charging in future scenarios: The effects of time of charging," Journal of Industrial Ecology, Yale University, vol. 25(5), pages 1250-1263, October.
    6. Yan, Shangyao & Tang, Ching-Hui, 2007. "A heuristic approach for airport gate assignments for stochastic flight delays," European Journal of Operational Research, Elsevier, vol. 180(2), pages 547-567, July.
    7. Shangyao Yan & Ching-Hui Tang, 2008. "An Integrated Framework for Intercity Bus Scheduling Under Stochastic Bus Travel Times," Transportation Science, INFORMS, vol. 42(3), pages 318-335, August.
    8. Overholm, Harald, 2015. "Spreading the rooftop revolution: What policies enable solar-as-a-service?," Energy Policy, Elsevier, vol. 84(C), pages 69-79.
    9. Larsen, Erik R. & Osorio, Sebastian & van Ackere, Ann, 2017. "A framework to evaluate security of supply in the electricity sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 646-655.
    10. Pfeiffer, Birte & Mulder, Peter, 2013. "Explaining the diffusion of renewable energy technology in developing countries," Energy Economics, Elsevier, vol. 40(C), pages 285-296.
    11. Suman, A., 2021. "Role of renewable energy technologies in climate change adaptation and mitigation: A brief review from Nepal," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    12. Anne-Maree Dowd & Michelle Rodriguez & Talia Jeanneret, 2015. "Social Science Insights for the BioCCS Industry," Energies, MDPI, vol. 8(5), pages 1-19, May.
    13. Fankhauser, Samuel & Jotzo, Frank, 2017. "Economic growth and development with low-carbon energy," LSE Research Online Documents on Economics 86850, London School of Economics and Political Science, LSE Library.
    14. Tilmann Rave, 2013. "Innovationsindikatoren zum globalen Klimaschutz – FuE-Ausgaben und Patente," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 66(15), pages 34-41, August.
    15. Daniel Moran & Richard Wood, 2014. "Convergence Between The Eora, Wiod, Exiobase, And Openeu'S Consumption-Based Carbon Accounts," Economic Systems Research, Taylor & Francis Journals, vol. 26(3), pages 245-261, September.
    16. Lykke E. Andersen & Luis Carlos Jemio, 2016. "Decentralization and poverty reduction in Bolivia: Challenges and opportunities," Development Research Working Paper Series 01/2016, Institute for Advanced Development Studies.
    17. Chen, Han & Huang, Ye & Shen, Huizhong & Chen, Yilin & Ru, Muye & Chen, Yuanchen & Lin, Nan & Su, Shu & Zhuo, Shaojie & Zhong, Qirui & Wang, Xilong & Liu, Junfeng & Li, Bengang & Tao, Shu, 2016. "Modeling temporal variations in global residential energy consumption and pollutant emissions," Applied Energy, Elsevier, vol. 184(C), pages 820-829.
    18. Inglesi-Lotz, Roula, 2017. "Social rate of return to R&D on various energy technologies: Where should we invest more? A study of G7 countries," Energy Policy, Elsevier, vol. 101(C), pages 521-525.
    19. Tom Mikunda & Tom Kober & Heleen de Coninck & Morgan Bazilian & Hilke R�sler & Bob van der Zwaan, 2014. "Designing policy for deployment of CCS in industry," Climate Policy, Taylor & Francis Journals, vol. 14(5), pages 665-676, September.
    20. Li, Yating & Fei, Yinxin & Zhang, Xiao-Bing & Qin, Ping, 2019. "Household appliance ownership and income inequality: Evidence from micro data in China," China Economic Review, Elsevier, vol. 56(C), pages 1-1.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:7:y:2014:i:9:p:5787-5824:d:39951. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.