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Demand-Side Management of Smart Distribution Grids Incorporating Renewable Energy Sources

Author

Listed:
  • Gerardo J. Osório

    (C-MAST, University of Beira Interior, Calcada Fonte Lameiro, 6201-001 Covilha, Portugal)

  • Miadreza Shafie-khah

    (INESC-TEC, R. Dr. Roberto Frias, 4200-465 Porto, Portugal)

  • Mohamed Lotfi

    (INESC-TEC, R. Dr. Roberto Frias, 4200-465 Porto, Portugal
    Faculty of Engineering, University of Porto, R. Dr. Roberto Frias, 4200-465 Porto, Portugal)

  • Bernardo J. M. Ferreira-Silva

    (Faculty of Engineering, University of Porto, R. Dr. Roberto Frias, 4200-465 Porto, Portugal)

  • João P. S. Catalão

    (INESC-TEC, R. Dr. Roberto Frias, 4200-465 Porto, Portugal
    Faculty of Engineering, University of Porto, R. Dr. Roberto Frias, 4200-465 Porto, Portugal)

Abstract

The integration of renewable energy resources (RES) (such as wind and photovoltaic (PV)) on large or small scales, in addition to small generation units, and individual producers, has led to a large variation in energy production, adding uncertainty to power systems (PS) due to the inherent stochasticity of natural resources. The implementation of demand-side management (DSM) in distribution grids (DGs), enabled by intelligent electrical devices and advanced communication infrastructures, ensures safer and more economical operation, giving more flexibility to the intelligent smart grid (SG), and consequently reducing pollutant emissions. Consumers play an active and key role in modern SG as small producers, using RES or through participation in demand response (DR) programs. In this work, the proposed DSM model follows a two-stage stochastic approach to deal with uncertainties associated with RES (wind and PV) together with demand response aggregators (DRA). Three types of DR strategies offered to consumers are compared. Nine test cases are modeled, simulated, and compared in order to analyze the effects of the different DR strategies. The purpose of this work is to minimize DG operating costs from the Distribution System Operator (DSO) point-of-view, through the analysis of different levels of DRA presence, DR strategies, and price variations.

Suggested Citation

  • Gerardo J. Osório & Miadreza Shafie-khah & Mohamed Lotfi & Bernardo J. M. Ferreira-Silva & João P. S. Catalão, 2019. "Demand-Side Management of Smart Distribution Grids Incorporating Renewable Energy Sources," Energies, MDPI, vol. 12(1), pages 1-23, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:1:p:143-:d:194334
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    References listed on IDEAS

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    1. Ghaderi, A. & Parsa Moghaddam, M. & Sheikh-El-Eslami, M.K., 2014. "Energy efficiency resource modeling in generation expansion planning," Energy, Elsevier, vol. 68(C), pages 529-537.
    2. Amrollahi, Mohammad Hossein & Bathaee, Seyyed Mohammad Taghi, 2017. "Techno-economic optimization of hybrid photovoltaic/wind generation together with energy storage system in a stand-alone micro-grid subjected to demand response," Applied Energy, Elsevier, vol. 202(C), pages 66-77.
    3. Siano, Pierluigi, 2014. "Demand response and smart grids—A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 461-478.
    4. Aghajani, G.R. & Shayanfar, H.A. & Shayeghi, H., 2017. "Demand side management in a smart micro-grid in the presence of renewable generation and demand response," Energy, Elsevier, vol. 126(C), pages 622-637.
    5. Wei Fan & Nian Liu & Jianhua Zhang, 2016. "An Event-Triggered Online Energy Management Algorithm of Smart Home: Lyapunov Optimization Approach," Energies, MDPI, vol. 9(5), pages 1-24, May.
    6. Selin Kocaman, Ayse & Abad, Carlos & Troy, Tara J. & Tim Huh, Woonghee & Modi, Vijay, 2016. "A stochastic model for a macroscale hybrid renewable energy system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 688-703.
    7. Siano, Pierluigi & Sarno, Debora, 2016. "Assessing the benefits of residential demand response in a real time distribution energy market," Applied Energy, Elsevier, vol. 161(C), pages 533-551.
    8. Thakur, Jagruti & Chakraborty, Basab, 2016. "Demand side management in developing nations: A mitigating tool for energy imbalance and peak load management," Energy, Elsevier, vol. 114(C), pages 895-912.
    9. Hu, Zheng & Kim, Jin-ho & Wang, Jianhui & Byrne, John, 2015. "Review of dynamic pricing programs in the U.S. and Europe: Status quo and policy recommendations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 743-751.
    10. Hepbasli, Arif, 2008. "A key review on exergetic analysis and assessment of renewable energy resources for a sustainable future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(3), pages 593-661, April.
    11. Nolan, Sheila & O’Malley, Mark, 2015. "Challenges and barriers to demand response deployment and evaluation," Applied Energy, Elsevier, vol. 152(C), pages 1-10.
    12. Hossain, M.S. & Madlool, N.A. & Rahim, N.A. & Selvaraj, J. & Pandey, A.K. & Khan, Abdul Faheem, 2016. "Role of smart grid in renewable energy: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1168-1184.
    13. Foster, Edward & Contestabile, Marcello & Blazquez, Jorge & Manzano, Baltasar & Workman, Mark & Shah, Nilay, 2017. "The unstudied barriers to widespread renewable energy deployment: Fossil fuel price responses," Energy Policy, Elsevier, vol. 103(C), pages 258-264.
    14. Guo, Peiyang & Li, Victor O.K. & Lam, Jacqueline C.K., 2017. "Smart demand response in China: Challenges and drivers," Energy Policy, Elsevier, vol. 107(C), pages 1-10.
    15. Muhammad Babar & Jakub Grela & Andrzej Ożadowicz & Phuong H. Nguyen & Zbigniew Hanzelka & I. G. Kamphuis, 2018. "Energy Flexometer: Transactive Energy-Based Internet of Things Technology," Energies, MDPI, vol. 11(3), pages 1-20, March.
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    Cited by:

    1. Sarker, Eity & Seyedmahmoudian, Mehdi & Jamei, Elmira & Horan, Ben & Stojcevski, Alex, 2020. "Optimal management of home loads with renewable energy integration and demand response strategy," Energy, Elsevier, vol. 210(C).
    2. Elizaveta Gavrikova & Yegor Burda & Vladimir Gavrikov & Ruslan Sharafutdinov & Irina Volkova & Marina Rubleva & Daria Polosukhina, 2019. "Clean Energy Sources: Insights from Russia," Resources, MDPI, vol. 8(2), pages 1-25, May.
    3. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).

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