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A real-time, personalized consumption-based pricing scheme for the consumptions of traditional and renewable energies

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  • Dai, Yeming
  • Sun, Xilian
  • Qi, Yao
  • Leng, Mingming

Abstract

A real-time, personalized consumption-based pricing scheme can induce electricity users to change their purchase behaviors, thus becoming an important issue in exploring the management mechanisms of electricity markets. To stabilize electricity prices, increase operators' revenues, and balance market demands, we consider the pricing scheme in a smart grid market where traditional and renewable energies are available for sales. Under the scheme, we develop a leader-follower game to characterize the strategic interactions between a demand side management center and residential users, and show that there exists a unique Stackelberg equilibrium. Our numerical analysis indicates that the real-time pricing scheme makes the electricity price difference between valley and peak times within 0.4 cents, thereby achieving the goal of mitigating peak loads and stabilizing electricity prices. We reveal that the renewable energy loads dominate traditional energy loads even when the price of renewable energy is higher than that of traditional energy. We also perform sensitivity analysis and find that an increase in a user's dissatisfaction with the electricity supply can raise electricity price for the user and two different electricity loads. Moreover, the demand side management center's revenue changes with a concave appearance.

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  • Dai, Yeming & Sun, Xilian & Qi, Yao & Leng, Mingming, 2021. "A real-time, personalized consumption-based pricing scheme for the consumptions of traditional and renewable energies," Renewable Energy, Elsevier, vol. 180(C), pages 452-466.
    • Handle: RePEc:eee:renene:v:180:y:2021:i:c:p:452-466
    DOI: 10.1016/j.renene.2021.08.085
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    1. Say, Nuriye Peker & Yucel, Muzaffer, 2006. "Energy consumption and CO2 emissions in Turkey: Empirical analysis and future projection based on an economic growth," Energy Policy, Elsevier, vol. 34(18), pages 3870-3876, December.
    2. Zareen, N. & Mustafa, M.W. & Sultana, U. & Nadia, R. & Khattak, M.A., 2015. "Optimal real time cost-benefit based demand response with intermittent resources," Energy, Elsevier, vol. 90(P2), pages 1695-1706.
    3. Srinivasan, Dipti & Rajgarhia, Sanjana & Radhakrishnan, Bharat Menon & Sharma, Anurag & Khincha, H.P., 2017. "Game-Theory based dynamic pricing strategies for demand side management in smart grids," Energy, Elsevier, vol. 126(C), pages 132-143.
    4. Yuan, Guanxiu & Gao, Yan & Ye, Bei, 2021. "Optimal dispatching strategy and real-time pricing for multi-regional integrated energy systems based on demand response," Renewable Energy, Elsevier, vol. 179(C), pages 1424-1446.
    5. Lu, Qing & Lü, Shuaikang & Leng, Yajun & Zhang, Zhixin, 2020. "Optimal household energy management based on smart residential energy hub considering uncertain behaviors," Energy, Elsevier, vol. 195(C).
    6. Gazijahani, Farhad Samadi & Salehi, Javad, 2018. "Reliability constrained two-stage optimization of multiple renewable-based microgrids incorporating critical energy peak pricing demand response program using robust optimization approach," Energy, Elsevier, vol. 161(C), pages 999-1015.
    7. Meng, Fanyi & Bai, Yang & Jin, Jingliang, 2021. "An advanced real-time dispatching strategy for a distributed energy system based on the reinforcement learning algorithm," Renewable Energy, Elsevier, vol. 178(C), pages 13-24.
    8. Klaus Eisenack & Mathias Mier, 2019. "Peak-load pricing with different types of dispatchability," Journal of Regulatory Economics, Springer, vol. 56(2), pages 105-124, December.
    9. Muqaddas Naz & Zafar Iqbal & Nadeem Javaid & Zahoor Ali Khan & Wadood Abdul & Ahmad Almogren & Atif Alamri, 2018. "Efficient Power Scheduling in Smart Homes Using Hybrid Grey Wolf Differential Evolution Optimization Technique with Real Time and Critical Peak Pricing Schemes," Energies, MDPI, vol. 11(2), pages 1-25, February.
    10. Bao, Xiongjiantao & Zhao, Wenhui & Wang, Xiaomei & Tan, Zhongfu, 2019. "Impact of policy mix concerning renewable portfolio standards and emissions trading on electricity market," Renewable Energy, Elsevier, vol. 135(C), pages 761-774.
    11. Ciwei Dong & Chi To Ng & T. C. E. Cheng, 2017. "Electricity Time-of-Use Tariff with Stochastic Demand," Production and Operations Management, Production and Operations Management Society, vol. 26(1), pages 64-79, January.
    12. Faria, P. & Vale, Z., 2011. "Demand response in electrical energy supply: An optimal real time pricing approach," Energy, Elsevier, vol. 36(8), pages 5374-5384.
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    2. Rajabzadeh, Hamed & Babazadeh, Reza, 2022. "A game-theoretic approach for power pricing in a resilient supply chain considering a dual channel biorefining structure and the hybrid power plant," Renewable Energy, Elsevier, vol. 198(C), pages 1082-1094.

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