مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion
مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

video

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

sound

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Version

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

View:

16
مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Download:

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Cites:

Information Journal Paper

Title

Profit increasing in smart grid market via actor-critic reinforcement learning

Author(s)

  Akbarian Amin Akbarian" target="_blank">Amin Akbarian Amin Akbarian | Akbarian Amin | Issue Writer Certificate 

Pages

  245-258

Abstract

 The electricity Smart grid market is complex and dynamic. Brokers, which mediate the sale of electrical power between retailers and wholesalers, are widely used in new markets for Smart grids. Due to the complexity and distribution properties of the market in Smart grid networks, multi-agent systems are appropriate to solve its problems. In these approaches, we have autonomous agents exchanging information with other agents all 24 hours of a day. These agents encounter major challenges including diverse consumption patterns of consumers, price changing according to consumption patterns, and the amount of electricity consumed during the day. In this paper our goal is to increase profit in the electricity grid market while modeling the components of the electricity market with multi-agent systems. In the proposed method, we first process the customer diversity using a sequential clustering method suitable for time series data. Then, for each cluster, we apply an active policy reinforcement learning algorithm named Actor-Critic reinforcement learning. Finally, we evaluate the impact of the reward shaping on the profit earnings and we offer an hourly tariff for each cluster according to their respective consumption time

Multimedia

  • No record.

    • Cites

  • No record.

    • References

  • No record.

    • Cite

    Related Journal Papers

  • No record.

    • Related Seminar Papers

  • No record.

    • Related Plans

  • No record.

    • Recommended Workshops






    Move to top
    telegram sharing button
    whatsapp sharing button
    linkedin sharing button
    twitter sharing button
    email sharing button
    email sharing button
    email sharing button
    sharethis sharing button