We present a new multi-agent model of generation expansion in electricity markets. The model simulates generation investment decisions of decentralized generating companies (GenCos) interacting in a complex, multidimensional environment. A probabilistic dispatch algorithm calculates prices and profits for new candidate units in different future states of the system. Uncertainties in future load, hydropower conditions, and competitors' actions are represented in a scenario tree, and decision analysis is used to identify the optimal expansion decision for each individual GenCo. We test the model using real data for the Korea power system under different assumptions about market design, market concentration, and GenCo's assumed expectations about their competitors' investment decisions.
A novel agent-based model, the Electricity Market Complex Adaptive System (EMCAS) model, is designed to study market restructuring and the impact of new technologies on the power grid. The agentbased approach captures the complex interactions between the physical infrastructure and the economic behaviors of various agents operating in an electricity market. The electric power system model consists of power generating plants, transmission lines, and load centers. The electric power market is composed of generating company agents who bid capacity and prices into power pools administered by an Independent System Operator (ISO). The ISO agent balances supply and demand for day-ahead markets. EMCAS also simulates real-time market operation to account for the uncertainties in day-ahead forecasts and availability of generating units. This paper describes the model, its implementation, and its use to address questions of congestion management, price forecasting, market design, and market power.
Future impacts on the electric power system in Illinois due to additional load from plug-in hybrid electric vehicles (PHEVs) are analyzed. The operation of the state's electric power system was simulated under a baseline scenario (i.e., without PHEVs) and three scenarios with PHEVs. The latter scenarios assumed different time periods throughout the day when PHEVs would be charging. The study assumed that PHEVs comprised 10% of the total number of light-duty vehicles on the road in 2020. Results of the simulations are presented, and the potential impacts of PHEV charging on the power system's generation mix and transmission network are identified. While the conclusions drawn are specific to the features of Illinois, the tools and methodologies employed can be applied to the study of power systems in other geographic areas.
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