Patent AU-2021107109-A4 (AU)
Title: Method and system for intelligent bid price selection for profit accumulation using multiple software agents and machine learning model
Inventor: KULKARNI GOPAL
IPC: G06Q30/06
Filed: 2021-08-25
Granted: 2021-12-02
Abstract: Method and system for intelligent bid price selection for profit 
accumulation using multiple software agents and machine learning 
model 
The present invention relates to a method and system for selecting optimal bids for a power 
generating company. More particularly, the field of the proposed instant invention is to 
build a complete automated system for prompt decision making in bidding price selection in 
diverse marketing operating conditions of an electricity or power market. The present 
invention mainly solves the technical problem exist in the art and provides an efficient and 
automatic method for bid price selection in diverse marketing condition using software 
agents and machine learning model. The present invention discloses a software agent-based 
structure which automatically performs the designated functions when predefined event 
triggers and employs mathematical bidding emulator, forecaster, supervised architecture, 
and complexity reduction module. The said system comprises of mathematical bidding 
emulator that closely model market sentiments in terms of mathematical functions and 
finally mimics the competitive bidding scenario for executing the settlement rules. An 
optimal bid calculator agent is employed in the system to evaluate the profitable bids 
offered by the generating company. This agent utilizes the diverse data provided by 
modelling agent to offer the optimal bids to the generating company. Further, the modeling 
data are provided to the supervised architecture for constructing an online decision-making 
architecture. The inputs of the system consist of rival bids for participating generating units 
and forecasted system demand. The multi-agent system consists of estimation layer, Control 
Action layer and Real time monitoring layer. Estimation layer consists of the estimation of 
rival bids and demand for anticipating a market scenario. Rival cost modelling and demand 
anticipating agents are involved in estimation layer. These agents can employ forecasting 
paradigms and advanced algorithmic processors for formulating market scenario and 
evaluation of the effective decision-making strategies. Further, the control and action layer 
employ intelligent decision-making protocols based on data driven mechanisms. The agents 
used in this is optimal bid calculator agent; this agent employ a powerful optimization 
algorithm for evaluating an objective function with the aim of maximizing the profit for a 
generating company. Further, the advanced layer such as real time monitoring layer 
anticipates the evaluated results of control and action layer using machine learning model.  
Further, it provides corrections to the anticipating bids. Complexity reduction software 
agent employs advanced processing techniques for reduction of the dimension of input 
matrices. Practices employed for dimensionality reduction are component analysis and 
Eigen analysis. Thus, an intelligent bidding price simulator is proposed by deep learning 
model of historical experiences by the means of Rival's bid data and demand anticipation in 
which maximum uncertainty is present. In this way, the proposed invention provides an 
Intelligent bidding price selection for profit accumulation using multiple software agents in 
power generation sector. The said method may also be employed to any sector bid prices 
selection.  
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Real Time 
Estimation Control & SimlTion 
Layer Action Layer laer 
Layer 
Modellingt Artificial Neural 
ModelingBased Agent Agent 
L Agent -AOptimal Bid 
Calculator 
DemandAgent Computational 
.nicpa. n Complexity 
Antptn Reduction 
Agentn 
Agen 
Figure 1: flow chart for the method for intelligent bid price selection for profit accumulation using 
multi software agents and machine learning model in accordance with the present invention 
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