Tata Consultancy Services Limited

Yogesh Kumar Bichpuriya

Venkatesh Sarangan

Narayanan Rajagopal

Avinash Achar

Easwara Naga Subramanian

Sanjay Purushottam Bhat

Abhay Pratap Singh

Akshaya Natarajan

Smita Sanjay Lokhande

Nilesh Sadashiv Hiremath

Vinodhkanna Jayaraman

Sivaramakrishnan Chandrasekaran

Aashutosh Kumar Soni

Sumit Kumar Ray

Subrata Indra

Subham Kumar

Vishnu Padmakumar Menon

Vivek Bandhu

Debarupa Das

This disclosure provides a system and method for flexibility based profit allocation for aggregator with distributed energy resources (DERs). The method of the present disclosure considers an aggregation model that allows an aggregator to orchestrate a set of heterogeneous DERs while enabling energy exchange among subscribed DERs and to participate in a day-ahead market. Further, a flexibility index is used to quantify the flexibility offered by a DER and a novel profit allocation model is proposed based on the flexibility index. Efficacy of the proposed models is demonstrated by evaluating their performance on a group of heterogeneous DERs using data traces from real-world electricity market.

System and method for flexibility based profit allocation for aggregator with distributed energy resources

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.

System and method for transactive energy market

Sum of bid quantities (across price bands) placed by generators in energy markets have been observed to be either constant OR varying over a few finite values. Several researches have used simulated data to investigate desired aspect. However, these approaches have not been accurate in prediction. Embodiments of the present disclosure identified two sets of generators which needed specialized methods for regression (i) generators whose total bid quantity (TBQ) was constant (ii) generators whose total bid quantity varied over a few finite values only. In first category, present disclosure used a softmax output based ANN regressor to capture constant total bid quantity nature of targets and a loss function while training to capture error most meaningfully. For second category, system predicts total bid quantity (TBQ) of a generator and then predicts to allocate TBQ predicted across the various price bands which is accomplished by the softmax regression for constant TBQs.

Neural network based prediction of competition behaviour in energy markets

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.

Method and system for building reinforcement learning (RL) based model for generating bids

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.

Systems and methods for simulation of electricity value ecosystem using agent based modeling approach

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Pune, India

Yogesh Kumar Bichpuriya