A Use of Multi-Agent Intelligent Simulator to Measure the Dynamics of US Wholesale Power Trade: A Case Study of the California Electricity Crisis

During the summer (2000), wholesale electricity prices in California were approximately 500% higher than those during the same months in 1998-1999. This study proposes a practical use of a reengineered Multi-Agent Intelligent Simulator (MAIS) to numerically examine several reasons on why the crisis has occurred during May 2000-Janurary 2001. The proposed MAIS generates artificially numerous trading agents equipped with different learning capabilities and duplicates their bidding strategies in the California electricity markets during the crisis period. In this study, we confirm the methodological validity of MAIS by comparing the estimation accuracy of MAIS with those of the three well-known computer science techniques (Support Vector Machines, Neural Networks and Genetic Algorithms). This study also investigates the dynamic change on agent composition in a time horizon. This investigation finds that all agents gradually shift to multiple learning capabilities so as to adjust themselves to the price fluctuation of electricity. Finally, we apply the sensitivity analysis of MAIS to identify economic rationales concerning the crisis. The sensitivity analysis results in the estimation accuracy (91.15%) during the crisis period. This study finds that 40.46% of the price increase during the crisis period was due to an increase in marginal production cost, 17.85% to traders’ greediness, 5.27% to a real demand change and 3.56% to market power. The remaining 32.86% came from other unknown market fundamentals and an estimation error. This numerical result indicates that the price hike has occurred due to an increase in fuel prices and real demand. The change of the two market fundamentals explained 45.73% (= 40.46% + 5.27%) of the price increase and fluctuation during the crisis. The responsibility of energy utility firms was 21.41% (= 17.85% + 3.56%).