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Optimal residential community demand response scheduling in smart grid

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  • Nan, Sibo
  • Zhou, Ming
  • Li, Gengyin

Abstract

With the reformation of electric power market and the development of smart grid technology, smart residential community, a new residential demand side entity, tends to play an important role in demand response program. This paper presents a demand response scheduling model for the novel residential community incorporating the current circumstances and the future trends of demand response programs. In this paper, smart residential loads are firstly classified into different categories according to various demand response programs. Secondly, a complete scheduling scheme is modeled based on the dispatch of residential loads and distributed generation. The presented model reduces the cost of user’s electricity consumption and decreases the peak load and peak-valley difference of residential load profile without bringing discomfort to the users, through which residential community can participate in demand response efficiently. Besides, this model can also provide support for the decision of electricity pricing strategies under power market development.

Suggested Citation

  • Nan, Sibo & Zhou, Ming & Li, Gengyin, 2018. "Optimal residential community demand response scheduling in smart grid," Applied Energy, Elsevier, vol. 210(C), pages 1280-1289.
    • Handle: RePEc:eee:appene:v:210:y:2018:i:c:p:1280-1289
    DOI: 10.1016/j.apenergy.2017.06.066
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    1. Bianchini, Gianni & Casini, Marco & Vicino, Antonio & Zarrilli, Donato, 2016. "Demand-response in building heating systems: A Model Predictive Control approach," Applied Energy, Elsevier, vol. 168(C), pages 159-170.
    2. Ferruzzi, Gabriella & Cervone, Guido & Delle Monache, Luca & Graditi, Giorgio & Jacobone, Francesca, 2016. "Optimal bidding in a Day-Ahead energy market for Micro Grid under uncertainty in renewable energy production," Energy, Elsevier, vol. 106(C), pages 194-202.
    3. Ratnam, Elizabeth L. & Weller, Steven R. & Kellett, Christopher M., 2015. "An optimization-based approach to scheduling residential battery storage with solar PV: Assessing customer benefit," Renewable Energy, Elsevier, vol. 75(C), pages 123-134.
    4. Alimohammadisagvand, Behrang & Jokisalo, Juha & Kilpeläinen, Simo & Ali, Mubbashir & Sirén, Kai, 2016. "Cost-optimal thermal energy storage system for a residential building with heat pump heating and demand response control," Applied Energy, Elsevier, vol. 174(C), pages 275-287.
    5. Rieger, Alexander & Thummert, Robert & Fridgen, Gilbert & Kahlen, Micha & Ketter, Wolfgang, 2016. "Estimating the benefits of cooperation in a residential microgrid: A data-driven approach," Applied Energy, Elsevier, vol. 180(C), pages 130-141.
    6. 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.
    7. Yin, Rongxin & Kara, Emre C. & Li, Yaping & DeForest, Nicholas & Wang, Ke & Yong, Taiyou & Stadler, Michael, 2016. "Quantifying flexibility of commercial and residential loads for demand response using setpoint changes," Applied Energy, Elsevier, vol. 177(C), pages 149-164.
    8. Graditi, G. & Ippolito, M.G. & Telaretti, E. & Zizzo, G., 2016. "Technical and economical assessment of distributed electrochemical storages for load shifting applications: An Italian case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 515-523.
    9. Zheng, Menglian & Meinrenken, Christoph J. & Lackner, Klaus S., 2015. "Smart households: Dispatch strategies and economic analysis of distributed energy storage for residential peak shaving," Applied Energy, Elsevier, vol. 147(C), pages 246-257.
    10. Cole, Wesley J. & Rhodes, Joshua D. & Gorman, William & Perez, Krystian X. & Webber, Michael E. & Edgar, Thomas F., 2014. "Community-scale residential air conditioning control for effective grid management," Applied Energy, Elsevier, vol. 130(C), pages 428-436.
    11. Shafie-khah, M. & Kheradmand, M. & Javadi, S. & Azenha, M. & de Aguiar, J.L.B. & Castro-Gomes, J. & Siano, P. & Catalão, J.P.S., 2016. "Optimal behavior of responsive residential demand considering hybrid phase change materials," Applied Energy, Elsevier, vol. 163(C), pages 81-92.
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