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Cost Estimation Process of Green Energy Production and Consumption Using Probability Learning Approach

Author

Listed:
  • Jian Xiao

    (Dong Fureng Economic & Social Development School, Wuhan University, Wuhan 430072, China)

  • Wei Hou

    (Chakrabongse Bhuvanarth International Institute for Interdisciplinary Studies, Rajamangala University of Technology Tawan-Ok, Bangkok 10400, Thailand
    State Grid Beijing Electric Power Corporation, Beijing 100031, China)

Abstract

With electric vehicle (EV) charging, green energy production costs could be reduced, and smart grid (SG) reliability improved. Nevertheless, the vast number of EVs could adversely affect the stability of the voltage and cost of operation. The present study designs a new security-based system based on a new EV participation charging method for a decentralized blockchain-enabled SG system. It is aimed at minimizing the level of power alternation in the electrical network and the total charging costs of EVs as mobile systems. In the first step, the power alternation level issue of the SG is formulated based on the capacity of EV batteries, the rate of charging, and EV users’ charging behavior. Next, a new adaptive blockchain-based EV participation (AdBEV) method is proposed, using the Iceberg order execution algorithm for improving EV discharging and charging schedules. Simulated outcomes demonstrate that the suggested method is superior to the genetic algorithm method when it comes to reducing power fluctuation levels and total charging cost.

Suggested Citation

  • Jian Xiao & Wei Hou, 2022. "Cost Estimation Process of Green Energy Production and Consumption Using Probability Learning Approach," Sustainability, MDPI, vol. 14(12), pages 1-14, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:12:p:7091-:d:835160
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    References listed on IDEAS

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