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Empowering end-use consumers of electricity to aggregate for demand-side participation


  • Melendez, Kevin A.
  • Subramanian, Vignesh
  • Das, Tapas K.
  • Kwon, Changhyun


End-use consumers (peers) are being empowered to aggregate for direct demand-side participation through load scheduling and energy sharing. This is the result of the growth of Internet of Things (IoT) enabled loads, availability of advanced metering infrastructure, and the move towards real-time (RT) pricing of electricity. Peer-to-peer (P2P) cooperation has received significant interest in recent years, though the focus of this growing body of research is on modeling prosumer behavior in microgrids. Hence, there is a need for new methodologies to examine empowerment of all end-use consumers (not limited to prosumers) to form aggregations and develop fair rules of cooperation to reduce cost. This paper offers an optimization based methodology to address the above need for power systems. It minimizes the total cost and considers fairness using a Nash bargaining approach. Since cost and fairness are often in conflict, trade-off strategies are also presented. The model to asses fairness is nonlinear. Hence, it is transformed into a second order cone program (SOCP) and solved using GUROBI software version 7.5.2. The methodology is implemented on a sample 5-bus network, built using price and demand data from one of the load zones of Pennsylvania, New Jersey, and Maryland (PJM) power network in the United States. It is shown that two aggregations of peers participating in the sample network can reduce their total cost by 14.17% and 22.7%, while maintaining fairness. Concluding remarks highlight some of the limitations of the methodology.

Suggested Citation

  • Melendez, Kevin A. & Subramanian, Vignesh & Das, Tapas K. & Kwon, Changhyun, 2019. "Empowering end-use consumers of electricity to aggregate for demand-side participation," Applied Energy, Elsevier, vol. 248(C), pages 372-382.
  • Handle: RePEc:eee:appene:v:248:y:2019:i:c:p:372-382
    DOI: 10.1016/j.apenergy.2019.04.092

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    References listed on IDEAS

    1. Subramanian, Vignesh & Das, Tapas K., 2019. "A two-layer model for dynamic pricing of electricity and optimal charging of electric vehicles under price spikes," Energy, Elsevier, vol. 167(C), pages 1266-1277.
    2. Li, Guoqing & Zhang, Rufeng & Jiang, Tao & Chen, Houhe & Bai, Linquan & Cui, Hantao & Li, Xiaojing, 2017. "Optimal dispatch strategy for integrated energy systems with CCHP and wind power," Applied Energy, Elsevier, vol. 192(C), pages 408-419.
    3. Gonçalves, Ivo & Gomes, Álvaro & Henggeler Antunes, Carlos, 2019. "Optimizing the management of smart home energy resources under different power cost scenarios," Applied Energy, Elsevier, vol. 242(C), pages 351-363.
    4. Nash, John, 1950. "The Bargaining Problem," Econometrica, Econometric Society, vol. 18(2), pages 155-162, April.
    5. Zhu, Jiawei & Lin, Yishuai & Lei, Weidong & Liu, Youquan & Tao, Mengling, 2019. "Optimal household appliances scheduling of multiple smart homes using an improved cooperative algorithm," Energy, Elsevier, vol. 171(C), pages 944-955.
    6. Aharon Ben-Tal & Arkadi Nemirovski, 2001. "On Polyhedral Approximations of the Second-Order Cone," Mathematics of Operations Research, INFORMS, vol. 26(2), pages 193-205, May.
    7. Natashia Boland & Hadi Charkhgard & Martin Savelsbergh, 2015. "A Criterion Space Search Algorithm for Biobjective Integer Programming: The Balanced Box Method," INFORMS Journal on Computing, INFORMS, vol. 27(4), pages 735-754, November.
    8. Natashia Boland & Hadi Charkhgard & Martin Savelsbergh, 2015. "A Criterion Space Search Algorithm for Biobjective Mixed Integer Programming: The Triangle Splitting Method," INFORMS Journal on Computing, INFORMS, vol. 27(4), pages 597-618, November.
    9. Lüth, Alexandra & Zepter, Jan Martin & Crespo del Granado, Pedro & Egging, Ruud, 2018. "Local electricity market designs for peer-to-peer trading: The role of battery flexibility," Applied Energy, Elsevier, vol. 229(C), pages 1233-1243.
    10. Long, Chao & Wu, Jianzhong & Zhou, Yue & Jenkins, Nick, 2018. "Peer-to-peer energy sharing through a two-stage aggregated battery control in a community Microgrid," Applied Energy, Elsevier, vol. 226(C), pages 261-276.
    11. Zhang, Chenghua & Wu, Jianzhong & Zhou, Yue & Cheng, Meng & Long, Chao, 2018. "Peer-to-Peer energy trading in a Microgrid," Applied Energy, Elsevier, vol. 220(C), pages 1-12.
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    Cited by:

    1. Tushar, Wayes & Saha, Tapan Kumar & Yuen, Chau & Azim, M. Imran & Morstyn, Thomas & Poor, H. Vincent & Niyato, Dustin & Bean, Richard, 2020. "A coalition formation game framework for peer-to-peer energy trading," Applied Energy, Elsevier, vol. 261(C).


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