MASCEM: Electricity Markets Simulation with Strategic Agents

• Published in: IEEE intelligent systems Vol. 26; no. 2; pp. 9 - 17
• Main Authors: Vale, Z, Pinto, T, Praça, I, Morais, H
• Format: Journal Article
• Language: English
• Published: Los Alamitos IEEE 01-03-2011; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Computer simulation; Decision support systems; Dynamical systems; Dynamics; Electricity; electricity markets; Electricity supply industry; Heuristic algorithms; Intelligent agents; Intelligent systems; Learning; machine learning; Markets; modeling and prediction; Multiagent systems; Power marketers; Power markets; Power system management; power systems; Predictive models; Reinforcement; Simulation; simulation support systems; Simulators
• ISSN: 1541-1672; 1941-1294
• DOI: 10.1109/MIS.2011.3

To study and understand this type of market, we developed the Multiagent Simulator of Competitive Electricity Markets (MASCEM) platform based on multiagent simulation. The MASCEM multiagent model includes players with strategies for bid definition, acting in forward, day-ahead, and balancing markets and considering both simple and complex bids. Our goal with MASCEM was to simulate as many market models and player types as possible. This approach makes MASCEM both a short and medium term simulation as well as a tool to support long-term decisions, such as those taken by regulators. This article proposes a new methodology integrated in MASCEM for bid definition in electricity markets. This methodology uses reinforcement learning algorithms to let players perceive changes in the environment, thus helping them react to the dynamic environment and adapt their bids accordingly.