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Electric energy systems have been restructuring for several years. Markets associated with a restructured electricity business have proven to be difficult to design and run because of their repetitive nature and the externalities provided by reliable grid operation. In a deregulated market environment, market participants will pursue their own profit maximizing objective rather than use an objective that reflects social benefit. When market rules are correct, both objectives should produce the same outcome. Testing a market design through simulation is an important step in the market design process. Multi-agent simulation is useful for gaining insights into market participant behavior under various rules. In this way market rules can be tested for efficacy and efficiency. A well-designed software agent that can represent the essence of individual's or a firms behavior is a very efficient way to explore outcomes. Due to the uncertainty and the complexity of electricity markets and when a real electric power network is present, software agents developed by others have found it difficult to fully emulate the behavior of human agents. Much of our research has been aimed at evaluating the results of additional policies, such as 1) requiring suppliers to hold forward contracts 2) adding fixed contracts for imported power, 3) increasing the base load capacity of suppliers and 4) wheeling policy effects on offer behavior. Overall, the results show that analytically derived software agents can replicate actual behavior in a deregulated electricity market, can provide new insights into how suppliers behave, and can evaluate a wide range of policy options for mitigating high prices.