Variant Roth-Erev Reinforcement Learning Algorithm-Based Smart Generator Bidding as Agents in Electricity Market

Abstract

The dynamically changing deregulated electricity market involves different entities and the aim of each entity is to achieve maximum profit while performing electricity price and power bidding. The agent-based modeling of electricity systems was used to model the market entities under whole sale electricity market operation. This paper discusses about the strategic learning ability of generators in an IEEE 30 bus system using Variant Roth-Erev learning algorithm. It also analyzes the variation in the generator commitments through the implemented learning algorithm during the present day schedule and helps the generator to perform smart bidding in the next electricity market operation. The results presented show that the smart generators are able to bid strategically in the electricity market and which will reflect in its net earnings in a market scheduled on a day-ahead basis.