Tata Consultancy Services Limited

Venkatesh Sarangan

Anand Sivasubramaniam

Arunchandar Vasan

Avinash Achar

Srinarayana Nagarathinam

Yogesh Kumar Bichpuriya

Rajesh Jayaprakash

Easwara Naga Subramanian

Ajay Narayanan

Narayanan Rajagopal

Sanjay Purushottam Bhat

Abhay Pratap Singh

Pandeeswari Indumathi Sankaranarayanan

Venkata Ramakrishna Padullaparthi

Prasant Kumar Misra

Arvind Ramanujam

Akshaya Natarajan

Shiva R Iyer

Venkata Ramakrishna P

Smita Sanjay Lokhande

Abinaya Manimaran

Anindya Pradhan

Iyswarya Narayanan

Venkataramakrishna P

Maathangi Sankar

Shravan Srinivasan

Gollakota Phani Bhargava Kaushik

Harihara Subramaniam Muralidharan

Kundan Kandhway

Vinodhkanna Jayaraman

Nilesh Sadashiv Hiremath

Sivaramakrishnan Chandrasekaran

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

Vehicle-to-Grid (V2G) technologies are being adopted to reduce peak demand and to take over as energy sources during grid instability. It is necessary to estimate attributes of electric vehicle trips and residual battery charge in order to correctly predict spatio-temporal availability of energy from EVs to form a micro grid. However, it may not be feasible to get the required attributes for all vehicles in a city-scale traffic scenario. Embodiments of the present disclosure and system address the problem of accurately estimating the local energy reserve that is available from parked EVs during a given time of the day. In addition, the system also determines which neighborhoods have the potential to form micro grids using the parked EVs during a given time period. This will help grid operator(s) to plan and design smart grids which can create EV-powered micro grids in neighborhoods during periods of peak demand or during disruptions.

Electric vehicle based micro grid identification for on-demand power supply

Traditionally, benchmarking of asset performance involves comparing actual performance with ideal values that correspond to test conditions which may not be realized in practice leading to inappropriate ranking of the assets. Systems and methods of the present disclosure use condition-aware reference curves for estimating the maximum possible operating efficiencies (under specific operating conditions) instead of the theoretical maximum efficiencies. The reference curves are received from the manufacturer or obtained from on-site test results. Benchmarking is then performed based on two dimensions, viz., an inter-asset metric and an intra-asset metric that are analogous to the first law and second law of thermodynamics respectively. The two-dimensional benchmarking then helps in identifying inefficient assets that may be analyzed further for finding the root cause. Tracking the performance of assets over time greatly helps in operations and maintenance, and thus reducing downtime of systems and accordingly the operating costs.

Methods and systems for benchmarking asset performance

The present disclosure provides a method and a system for estimating capacity and usage pattern of behind-the-meter energy storage in electric networks. Conventional techniques on estimating an effective capacity of behind-the-meter energy storage of a consumer, in presence of distributed energy generation units is limited, computationally intensive and provide inaccurate prediction. The present disclosure provides an accurate estimate of the effective capacity and usage pattern of behind-the-meter energy storage of a target consumer utilizing data samples received from a utility in presence of one or more distributed energy generation units, using an energy balance equation with less computation and accurate prediction. Based on accurate estimation of the effective capacity and usage pattern, the utility may plan for proper infrastructure to meet power demands of the consumers.

Estimating capacity and usage pattern of behind-the-meter energy storage in electric networks

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

A system and method for predicting travel time of a vehicle on routes of unbounded length within arterial roads. It collects historical information from probe vehicles positions using GPS technology in a periodic fashion and the sequence of links traversed between successive position measurements. Further, it collects information of neighborhood structure for each link within the arterial roads network. Any of the existing conditional probability distribution functions could be used to capture the spatio-temporal dependencies between each link of the arterial network and its neighbors. It learns the parameters of this data driven probabilistic model from historical information of probe vehicle trajectories traversed within the arterial roads network using an associated expectation maximization method. Finally it predicts travel time of vehicles on routes of unbounded length in a novel fashion within the network of arterial roads using the learnt parameters and current real time information of trajectories of vehicle.

Predicting vehicle travel time on routes of unbounded length in arterial roads

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

Electrical utilities offer incentives to customers to reduce consumption during periods of demand-supply mismatch. A building's participation in demand response (DR) depends both on its ability (due to building constraints), and its willingness (a function of incentive) to reduce electricity. Customers prefer a large incentive whereas a utility would want to minimize the revenue outflow to achieve a target reduction. Systems and methods of the present disclosure identify optimal incentive from the utility's perspective reflecting this trade-off. A model is built to estimate the demand response potential (DRP) of a building for a given incentive offered by the utility. The models for individual buildings are used to characterize the behavior of an ensemble of buildings. The utility may then decide optimum incentives that should be offered to achieve a target DR, using the associated DRP.

Systems and methods for optimizing incentives for demand response

The present disclosure provides system and method for determining optimal decision parameters for a demand response (DR) event involving a District Cooling Plant (DCP). Most of conventional DR event techniques address control of building-level energy consumption loads alone while in presence of District Cooling (DC) has not received much attention when a plurality of buildings are served by a District Cooling Plant (DCP). The disclosed system and method determine set points of optimal decision parameters of the plurality of buildings and the DCP, by conditioning and un-conditioning on the DCP parameters such that a thermal discomfort of occupants residing in the plurality of buildings is minimum and achieves a maximum target energy demand reduction during the DR event. The disclosed system and method work for hundreds of buildings and able to determine the optimal decision parameters for each building and the DCP efficiently.

Determining optimal decision parameters for a demand response event involving a district cooling plant

Method and system for predicting temporal-spatial distribution of load demand on an electric grid due to a plurality of Electric Vehicles (EVs) is described. The method includes creating an EV load demand (EVLD) model for a Region of Interest (ROI) serviced by the electric grid, wherein the EVLD model integrates an EV model and a transport simulator simulating EV traffic conditions for the ROI. Further, the method includes computing the load demand in time and space in terms of State of Charge (SOC) of a battery for each EV among the plurality of EVs in the ROI, based on the EVLD model. Furthermore, the method includes aggregating the computed the load demand, in terms of the SOC, of each EV in time domain and space domain to create a temporal-spatial impact of the load demand by the plurality of EVs on the electric grid for the ROI.

Method and system providing temporal-spatial prediction of load demand

A system and method for predicting travel time of a vehicle on one or more routes within arterial roads. It collects historical information from probe vehicles positions using GPS technology in a periodic fashion and the sequence of links traversed between successive position measurements. Further, it collects information of neighborhood structure for each link within the arterial roads network. A NoisyOR conditional probability distribution function is proposed to capture the spatio-temporal dependencies between each link of the arterial network and its neighbors. It learns the parameters of this data driven probabilistic model from collected historical information of probe vehicle trajectories traversed within the arterial roads network using a proposed expectation maximization method. Finally it predicts travel time of vehicles on routes within the arterial roads using the learnt parameters and real time information of trajectories of vehicle that have been recorded from the arterial roads network using GPS technology sensing.

Predicting vehicle travel times by modeling heterogeneous influences between arterial roads

Conventional systems for monitoring pipe networks are generally not scalable, impractical in the field with uncontrolled environments or rely of static features of pipes that are vary depending on the pipes under consideration. The ideal sensor-ed monitoring systems are not economically viable. Systems and methods of the present disclosure provide an improved data-driven model to rank pipes in the order of burst probabilities, by including dynamic feature values of pipes such as pressure and flow that depends on network structure and operations. The present disclosure enables estimating approximate values for the dynamic features since they are hard to estimate accurately in the absence of a calibrated hydraulic model. The present disclosure also validates the estimated approximate dynamic feature values for the purpose of estimating bursts likelihood vis-a-vis accurate values of the dynamic metrics.

Ranking pipes for maintenance in pipe networks using approximate hydraulic metrics

A technique for energy sample forecasting of heating, venting and air conditioning-refrigeration (HVAC-R) systems is disclosed. In an example, a first expected energy sample of a HVAC-R system at a first time period is forecasted by modelling actual energy samples of the HVAC-R system at previous time periods using a statistically-based seasonal-autoregressive integrated moving average (SARIMA) model. Further, an anomaly is detected at the first time period when deviation between the first expected energy sample and an actual energy sample at the first time period is greater than a dynamic context sensitive threshold. Also, an expected energy sample at next time period is forecasted by modelling a second expected energy sample of the HVAC-R system at the first time period using the statistically-based SARIMA model upon detecting anomaly. The second expected energy sample is forecasted by modelling the actual energy samples at the previous time periods using a physical model.

System and method for energy sample forecasting of HVAC-R systems

Devices and methods comprise determining an actual weather independent energy consumption value and actual weather dependent energy consumption value of a building. The devices and methods further comprise, obtaining energy simulation information from a data source. Further, an ideal weather independent energy consumption value and one or more ideal loads are generated based on the energy simulation information, building information, and total energy consumption of the building. The devices and methods further comprise determining a benchmarking score for the building using the actual weather independent energy consumption value, the actual weather dependent energy consumption value, the ideal weather independent energy consumption value, and the one or more ideal loads for managing energy consumption associated with the building.

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Chennai, India

Venkatesh Sarangan