Publication

Abstract : The electricity market operation in smart grid requires a certain level of intelligence by the agents. The operation of multiple agents in the market is different under various scenarios where the machine learning approach exhibited by the agents plays a major role. Each trader is focused on achieving high profit yielding and a positive reward by experimenting various cost parameters and propensity levels. The best learning model for smart grid incorporating the deregulated market structure is reinforcement learning. In this type of model, the agent takes action in an environment based on the previous experience and improvises automatically in the successive bidding environment. This help the generator companies to maximize their profit and achieve smart bidding environment for efficient smart grid transactions. Once the generator fails to meet the requirement and propose greater price, penalization also becomes a part to assess the bidding strategy in electricity market through agent-based machine learning. A new interactive Variant Roth-Erev algorithm based approach is implemented in various agents, and the test system considered for the entire analysis is an IEEE 5-bus system. The system is modelled taking into account different propensity levels and stopping rules. Congestion handling in the system is also an objective irrespective of earning high profit. Further, the analysis is extended for a period of 50 days to showcase the effectiveness of the approach. The results obtained for different cases are investigated in detailed way through comparison without learning and with machine learning approach using Java-based programming.