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A hybrid price-based demand response program for the residential micro-grid

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  • Monfared, Houman Jamshidi
  • Ghasemi, Ahmad
  • Loni, Abdolah
  • Marzband, Mousa

Abstract

During the past two decades, providing solutions to enhance the efficiency of power systems, like optimal consumption management has been attracting a good deal of attention. Demand Response (DR) programs, have always been among the appropriate ways to persuade consumers to alter consumption patterns. In the main, the implementation of DR programs is carried out by price-based and incentive-based strategies. In this paper, first, a brief overview of the smart grid principles on retail electricity pricing is presented. Then, a hybrid price-based demand response (HPDR) is proposed, which is more adaptable to pricing principles compared to other existing strategies. This strategy is implemented in day-ahead scheduling of a residential microgrid. Moreover, to increase the accuracy of the proposed model, the uncertainty regarding decision variables and parameters including the generation units, load dispatch in the Micro-grid is considered. Finally, the results of numerical studies show the effectiveness of the proposed retail pricing strategy, and demonstrate a decrease in Peak-to-Valley (PtV) index and Coefficient of Variation Percentage (CVP) by almost 12% and 25% as well as an increase in social welfare indicator, power sale at peak times, respectively, by approximately 18%, 24%, and 25% in comparison with other methods.

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  • Monfared, Houman Jamshidi & Ghasemi, Ahmad & Loni, Abdolah & Marzband, Mousa, 2019. "A hybrid price-based demand response program for the residential micro-grid," Energy, Elsevier, vol. 185(C), pages 274-285.
    • Handle: RePEc:eee:energy:v:185:y:2019:i:c:p:274-285
    DOI: 10.1016/j.energy.2019.07.045
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    14. Shen, Ziqi & Wei, Wei & Wu, Lei & Shafie-khah, Miadreza & Catalão, João P.S., 2021. "Economic dispatch of power systems with LMP-dependent demands: A non-iterative MILP model," Energy, Elsevier, vol. 233(C).
    15. Wang, Ziyang & Sun, Mei & Gao, Cuixia & Wang, Xin & Ampimah, Benjamin Chris, 2021. "A new interactive real-time pricing mechanism of demand response based on an evaluation model," Applied Energy, Elsevier, vol. 295(C).
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    18. Wen, Lulu & Zhou, Kaile & Li, Jun & Wang, Shanyong, 2020. "Modified deep learning and reinforcement learning for an incentive-based demand response model," Energy, Elsevier, vol. 205(C).
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    21. Incheol Shin, 2020. "Approximation Algorithm-Based Prosumer Scheduling for Microgrids," Energies, MDPI, vol. 13(21), pages 1-16, November.
    22. Guo, Li & Hou, Ruosong & Liu, Yixin & Wang, Chengshan & Lu, Hai, 2020. "A novel typical day selection method for the robust planning of stand-alone wind-photovoltaic-diesel-battery microgrid," Applied Energy, Elsevier, vol. 263(C).
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    24. Liu, Youquan & Li, Huazhen & Zhu, Jiawei & Lin, Yishuai & Lei, Weidong, 2023. "Multi-objective optimal scheduling of household appliances for demand side management using a hybrid heuristic algorithm," Energy, Elsevier, vol. 262(PA).

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