An agent based system for california electricity market: a perspective of myopic machine learning

Abstract

In recent years, an agent based system is widely adopted to model a deregulated electricity market. [1] and [2] have developed a multi-agent intelligent simulator (MAIS) to model the structure of US wholesale market. The methodological practicality was confirmed with a simulation study and a real data set from PJM electricity market. In our proposed artificial wholesale market, the agents are equipped with limited reinforcement learning capabilities. We validate the agent based model with the help of six data sets from the California electricity market. The performance of the MAIS is compared with other well-known methods, using a real data set on power trading related to the California electricity (2000-2001).