The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Oct. 18, 2022

Filed:

Jun. 10, 2020
Applicant:

Tata Consultancy Services Limited, Mumbai, IN;

Inventors:

Easwara Subramanian, Hyderabad, IN;

Avinash Achar, Chennai, IN;

Yogesh Kumar Bichpuriya, Pune, IN;

Sanjay Purushottam Bhat, Hyderabad, IN;

Akshaya Natarajan, Chennai, IN;

Venkatesh Sarangan, Chennai, IN;

Abhay Pratap Singh, Sagar, IN;

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
H02J 3/00 (2006.01); G06N 20/00 (2019.01); G06Q 30/02 (2012.01); G06N 3/08 (2006.01); G06F 17/11 (2006.01); G06Q 10/06 (2012.01); G06Q 30/06 (2012.01);
U.S. Cl.
CPC ...
H02J 3/008 (2013.01); G06F 17/11 (2013.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); G06Q 10/0631 (2013.01); G06Q 30/02 (2013.01); G06Q 30/0202 (2013.01); G06Q 30/0206 (2013.01); G06Q 30/06 (2013.01); H02J 3/003 (2020.01);
Abstract

In energy markets in which bidding process is used to sell energy, it is important that a mechanism for deciding bidding amount is in place. State of the art systems in this domain have the disadvantage that they rely on simulation data, and also they make certain assumptions, and both the factors can affect accuracy of results when the systems are deployed and are expected to handle practical scenarios. The disclosure herein generally relates to energy markets, and, more particularly, to a method and a system for Reinforcement Learning (RL) based model for generating bids. The system trains a RL agent using historical data with respect to competitor bids places and Market Clearing Prices (MCPs). The RL agent then processes real-time inputs and generates bidding recommendations.


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