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公开(公告)号：US11409925B2
公开(公告)日：2022-08-09
申请号：US16843031
申请日：2020-04-08
发明人： Yogesh Kumar Bichpuriya , Venkatesh Sarangan , Sivaramakrishnan Chandrasekaran , Narayanan Rajagopal , Nilesh Sadashiv Hiremath , Vinodhkanna Jayaraman
摘要： This disclosure relates to methods and systems for simulation of electricity value ecosystem using agent based modeling approach. State-of-the-art methods utilize simulation tools to support decision making that do not model agents own behaviour and its response to other agents based on an interaction, thereby unable to analyse complex interactions in the electricity value ecosystem. The present disclosure provides a generalized integrated simulation platform which provides dynamic configurability to simulate a plurality of user requirements associated with the electricity value eco-system using a causal diagram which is further used to identify a plurality of agents. Further, a plurality of a plurality of models and processes for the plurality of agents are determined or generated based on their availability in a repository. The causal diagram is refined in accordance with one or more constraints which supports in making a better and informed decision considering changing dynamics of the plurality of agents.
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2.发明授权公开(公告)号：US11476669B2
公开(公告)日：2022-10-18
申请号：US16897842
申请日：2020-06-10
发明人： Easwara Subramanian , Avinash Achar , Yogesh Kumar Bichpuriya , Sanjay Purushottam Bhat , Akshaya Natarajan , Venkatesh Sarangan , Abhay Pratap Singh
摘要： 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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公开(公告)号：US11817706B2
公开(公告)日：2023-11-14
申请号：US17203817
申请日：2021-03-17
发明人： Narayanan Rajagopal , Yogesh Kumar Bichpuriya , Sumit Kumar Ray , Aashutosh Kumar Soni , Subrata Indra , Subham Kumar , Vishnu Padmakumar Menon , Smita Lokhande
摘要： This disclosure relates generally to a system and method for transactive energy (TE) market model. Existing TE models either consider market without a network simulation model or both the market model and the network simulation model are considered in a single formulation which makes the computation complex. The disclosed system considers both the power flow simulation of the network and the market model in a sequence. In other words, the disclosed system decouples the market model and network model to reduce the computational complexity at the same time without sacrificing on the technical feasibility of the solution.
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公开(公告)号：US20210296896A1
公开(公告)日：2021-09-23
申请号：US17203817
申请日：2021-03-17
发明人： Narayanan Rajagopal , Yogesh Kumar Bichpuriya , Sumit Kumar Ray , Aashutosh Kumar Soni , Subrata Indra , Subham Kumar , Vishnu Padmakumar Menon , Smita Lokhande
IPC分类号： H02J3/00
摘要： This disclosure relates generally to a system and method for transactive energy (TE) market model. Existing TE models either consider market without a network simulation model or both the market model and the network simulation model are considered in a single formulation which makes the computation complex. The disclosed system considers both the power flow simulation of the network and the market model in a sequence. In other words, the disclosed system decouples the market model and network model to reduce the computational complexity at the same time without sacrificing on the technical feasibility of the solution.
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