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Abstract
Energy continues to be an important topic to consumers, investors, policy makers, and the public at large. High gasoline and natural gas prices, rising electricity costs and occasional blackouts, persistent reliance on foreign and potentially unstable sources of oil, and the role that greenhouse gas emissions from vehicles and coal-fired power plants play in global climate change, are just a few of the public concerns that are directly related to energy. This paper describes the use of Mathematica to model the integrated energy system and markets, using an agent-based simulation modeling approach based on the cellular automata paradigm. An implementation-independent structured modeling approach, similar to object-oriented modeling, is used within the Mathematica programming environment. Abstract data types are defined to represent energy system objects, in general, and energy system agents, in particular. The structured modeling approach has several advantages including scalability, reusability, and consistency. Mathematica's interactive notebook style allows for the quick exploration and interactive analysis of large-scale databases for the energy system. Interactive analysis is used for data cleaning and visualization in support of data verification. Energy system data and results are exported to 3D graphics for use in Live3D. The simulation framework built in Mathematica allows for the easy setup of large-scale parameter sweeps over higher-dimensional spaces to explore system behaviors across the realm of all possibilities. The energy system is a complex network of integrated fuel cycles and energy processes. The energy systems model is comprised of models of the individual fuel cycles including petroleum, natural gas, coal, electricity, and nuclear. The approach is to model the energy system from the "ground up," assembling the larger model of the energy system from individual models of physical processes and behavioral models of individual decision-making agents. The rules of business and social interaction are at least as important as the rules of physics when it comes to the production, processing transport, and delivery of energy. Using the agent approach, decisions based on finance and economics are interwoven with constraints such as those presented by environmental and safety factors that facilitate or impede the evolution of the energy system. In addition to modeling physical flows of energy in the physical systems level, agent decision-making behaviors are consolidated into a business layer. A mapping between the business layer and the physical systems layer indicates ownership patterns for energy facilities, which is publicly available information. Ownership patterns set up the decision-making processes affecting the energy system. In addition to energy flows, revenue flows will be modeled at the individual process level or, as appropriate, at more aggregate decision-making levels that reflect the financial situation of energy industrial organization. Revenue streams generate capital for capacity expansion, or alternatively force industries to contract if they are not able to maintain competitive positions.
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