An agent-based approach with collaboration among agents: Estimation of wholesale electricity price on PJM and artificial data generated by a mean reverting model

a r t i c l e i n f o This study examines the performance of MAIS (Multi-Agent Intelligent Simulator) equipped with various learning capabilities. In addition to the learning capabilities, the proposed MAIS incorporates collaboration among agents. The proposed MAIS is applied to estimate a dynamic change of wholesale electricity price in PJM (Pennsylvania– New Jersey–Mainland) and an artificial data set generated by a mean reverting model. Using such different types of data sets, the methodological validity of MAIS is confirmed by comparing it with other well-known alternatives in computer science. This study finds that the MAIS needs to incorporate both the mean reverting model and the collaboration behavior among agents in order to enhance its estimation capability. The MAIS discussed in this study will provide research on energy economics with a new numerical capability that can investigate a dynamic change of not only wholesale electricity price but also speculation and learning process of traders. An agent-based approach is a numerical method to deal with various types of system complexities in natural and social sciences. Reinforcement learning is often incorporated into software agents so that they can interact with a dynamics of environment (Abul et al., 2000, Kaya and Alhajj, 2005). The application of an agent-based approach provides us with a new type of numerical capability to understand a dynamic change of a market and adaptive behaviors of traders who participate in the market. Such applicability is confirmed in power trading. examined the dynamic change 1 of a power exchange market from the perspective of a multi-agent adaptive system. This group of research opened up a new approach for dealing with business complexity of power trading. However, the previous studies described only the development of modeling and simulation. Their agents incorporated only a single parameter to express reinforcement learning. Moreover, they are not equipped with an estimation capability to predict the market price of electricity. Consequently, the conventional use of an agent-based approach has a limit on practicality because real power trading needs multiple parameters to function reinforcement learning on bidding quantity and price.,b,c) investigated various types of power trading agents equipped with different learning capabilities. The software for the agent-based approach was referred to as " MAIS (Multi-Agent Intelligent Simulator) ". The first research (2005) proposed a multi-agent system that incorporated learning capabilities into agents who trade wholesale electricity. The learning capabilities incorporated in the study were …