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The real-time pricing optimization model of smart grid based on the utility function of the logistic function

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  • Li, Yuanyuan
  • Li, Junxiang
  • He, Jianjia
  • Zhang, Shuyuan

Abstract

The utility function is very significant for solving the real-time pricing problem of smart grid. Based on the Logistic function, a new utility function is constructed to satisfy four properties of the utility function. In addition, from the perspective of social welfare, the real-time pricing optimization model of smart grid is established. By using the KKT conditions and the improved Fischer-Burmerister smoothing function, the optimization model is transformed into a smoothing equations problem and the smoothing Newton algorithm is used to obtain the optimal solution of the problem. The nonsingularity of the Jacobian matrix and the global convergence of the algorithm are proved. The simulation results show that, compared with previous quadratic and logarithmic utility functions, the new utility function can not only reduce the user’s electricity consumption and the supplier’s cost can but also improve the user’s utility and the total social welfare, which also indicates that the new utility function is effective in establishing the real-time pricing model of smart grid. Furthermore, the iteration times of several algorithms to solve the real-time pricing problem of smart grid are compared, which showed that the convergence rate of the smoothing Newton algorithm is very fast.

Suggested Citation

  • Li, Yuanyuan & Li, Junxiang & He, Jianjia & Zhang, Shuyuan, 2021. "The real-time pricing optimization model of smart grid based on the utility function of the logistic function," Energy, Elsevier, vol. 224(C).
    • Handle: RePEc:eee:energy:v:224:y:2021:i:c:s0360544221004217
    DOI: 10.1016/j.energy.2021.120172
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    1. Deng, Tingting & Yan, Wenzhou & Nojavan, Sayyad & Jermsittiparsert, Kittisak, 2020. "Risk evaluation and retail electricity pricing using downside risk constraints method," Energy, Elsevier, vol. 192(C).
    2. 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.
    3. Hong, Seung Ho & Yu, Mengmeng & Huang, Xuefei, 2015. "A real-time demand response algorithm for heterogeneous devices in buildings and homes," Energy, Elsevier, vol. 80(C), pages 123-132.
    4. Alipour, Manijeh & Zare, Kazem & Seyedi, Heresh & Jalali, Mehdi, 2019. "Real-time price-based demand response model for combined heat and power systems," Energy, Elsevier, vol. 168(C), pages 1119-1127.
    5. Nojavan, Sayyad & Zare, Kazem & Mohammadi-Ivatloo, Behnam, 2017. "Robust bidding and offering strategies of electricity retailer under multi-tariff pricing," Energy Economics, Elsevier, vol. 68(C), pages 359-372.
    6. Muhammad Awais & Nadeem Javaid & Khursheed Aurangzeb & Syed Irtaza Haider & Zahoor Ali Khan & Danish Mahmood, 2018. "Towards Effective and Efficient Energy Management of Single Home and a Smart Community Exploiting Heuristic Optimization Algorithms with Critical Peak and Real-Time Pricing Tariffs in Smart Grids," Energies, MDPI, vol. 11(11), pages 1-30, November.
    7. Yanzhe (Murray) Lei & Stefanus Jasin, 2020. "Real-Time Dynamic Pricing for Revenue Management with Reusable Resources, Advance Reservation, and Deterministic Service Time Requirements," Operations Research, INFORMS, vol. 68(3), pages 676-685, May.
    8. Severin Borenstein, 2005. "The Long-Run Efficiency of Real-Time Electricity Pricing," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 93-116.
    9. Yang, Yandong & Hong, Weijun & Li, Shufang, 2019. "Deep ensemble learning based probabilistic load forecasting in smart grids," Energy, Elsevier, vol. 189(C).
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    5. Costa, Vinicius B.F. & Bonatto, Benedito D. & Silva, Patrícia F., 2022. "Optimizing Brazil's regulated electricity market in the context of time-of-use rates and prosumers with energy storage systems," Utilities Policy, Elsevier, vol. 79(C).
    6. Emad M. Ahmed & Rajarajeswari Rathinam & Suchitra Dayalan & George S. Fernandez & Ziad M. Ali & Shady H. E. Abdel Aleem & Ahmed I. Omar, 2021. "A Comprehensive Analysis of Demand Response Pricing Strategies in a Smart Grid Environment Using Particle Swarm Optimization and the Strawberry Optimization Algorithm," Mathematics, MDPI, vol. 9(18), pages 1-24, September.

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