Abstract: This disclosure relates generally to optimal intraday scheduling of aggregated Distributed Energy Resources (DERs). Owing to their stochastic nature, DERs aggregators are more suited to participate in intraday electricity markets. The current works on DER aggregators trading in intraday markets do not satisfactorily model the different aspects. The disclosure is an optimal trading strategy for aggregators managing heterogeneous DERs to participate in intraday markets. The intraday market is modelled using a joint price-volume dynamics distribution and an optimal bidding strategy is disclosed for the trades/bids placed earlier to be corrected based on the revised forecasts of demand and generation while allowing for energy exchanges within the DER pool. Further the optimal bidding strategy of aggregators in an intraday market is a MINLP problem, which is solved by converting the complex non-linearities in the problem into a coupled MILP—simple maximization set-up, which is then solved in an iterative fashion.

Abstract: Owing to their stochastic nature, Distributed Energy Resources (DERs) are more suited to participate in short-term or intraday electricity markets. However, it is very difficult for an asset owner to manage their operation when interacting with markets and create operation schedules. Present disclosure provides systems and methods that for the trades/bids placed earlier to be corrected based on the revised forecasts of demand and generation in the DER pool. The system models an optimal bidding problem of aggregators in an intraday market as a mixed-integer non-linear programming (MINLP) problem. The MINLP problem is converted to an NLP problem by an optimization model and integer variables are relaxed to solve the NLP problem and obtain (i) an optimal intraday operating schedule for one or more DERs, and (ii) an intraday bid associated with the DERs for a plurality of delivery slots to be traded in an intraday market.

Abstract: 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.

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.

Abstract: 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.

Abstract: 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.

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.

Abstract: 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.