CDI-Type II: Energy Policy, Investment, and Pricing Analysis Driven by Computational Steering

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1029337SioshansiAbstractConcerns about the U.S.'s reliance on fossil fuels have increased the incentive to reduce this dependence. Increased renewable energy, energy storage, and plug-in hybrid vehicles (PHEVs) and electric vehicles (EVs) are often suggested as parts of the solution. However, widespread use of these technologies will have a profound effect on the U.S. power grid. It is therefore important to understand economic and policy questions regarding the interplay of PHEVs, EVs, renewable energy, energy storage, distributed generation, and the grid. Integrated models that allow the simulation of policies under different sets of assumptions are crucial to understanding the complexity and emergent behaviors of such systems. The intersection of energy, environmental, and national security issues involve a complex set of trade-offs among initial cost, local air quality, climate change, technology, and energy independence. Without understanding the interactions of the different scales and different sectors of energy production, only partial and overly simplistic solutions can be analyzed. This project will create a comprehensive model of next-generation power systems that integrates the economic decisions faced by both individuals and the power grid?called the Integrated Computational System for Energy Pricing and Policy (ICS-EPP). The main project outcome will be the ICS-EPP model that includes a typical power grid (such as that of the U.S.) and autonomous sub-models of individuals to efficiently simulate different scenarios of the evolution of such a system under different economic policies and technology options. The ICS-EPP will be designed so that important questions can be addressed, such as the cost of adding PHEVs and EVs to the grid, the value of vehicle to grid services, or the impacts of different pricing structures on energy use and investment. The comprehensive nature of ICS-EPP permits examination of the side effects of various actions: changes in emissions and demand for generating capacity, for example. More important, as policy-makers debate various mechanisms to reduce GHG emissions and reduce dependence on fossil fuels, they will have available a more accurate view of the cost and efficacy of various policy options. State and local governments can examine the value of incentivizing different technologies such as PHEV charging stations, household solar panels, and/or integrated heat and power units. This model will permit policy makers and analysts to conduct virtual experiments of the interaction of households, energy technologies, and the power system at a new level of detail. The project leverages a broad collaboration that includes engineers, economists, computational scientists, as well as automotive and electric power industry leaders.Intellectual Merit:This project relies on computational steering to knit together and solve models of households and the power grid into a large-scale agent-based model. Such modeling has not been attempted, due to the size and computational power needed to accommodate the subtleties of the markets and solve such a model. The work is based on the premise that computational modeling and simulation can serve as an effective means of evaluating the potential effects of different economic policies. This will require advances in a number of areas: (a) the ability to integrate models of diverse systems based on different simulation methodologies into an integrated whole; (b) techniques for information from large simulation results that can be matched against qualitative objectives; and (c) running large, heterogeneous models at multiple time scales on currently prevalent High Performance Computing systems.Broader Impact:The success of this project will enable paradigm-shifting advances in energy policy analysis, as decisionmakers can examine the results, in multiple dimensions (price; GHG, SOx, and NOx emissions; petroleum consumption; household expenditures; etc.), of policies that target or affect energy, such as tax credits for PHEV purchase or charging infrastructure, GHG emissions taxes or caps, or the synergies of integrating PHEVs with solar and wind generation. Also, a low-level computational steering toolkit that can be applied to other domain areas will be created. Research will also be integrated into economics, engineering, and computer science curricula in the form of interdisciplinary courses.Program Officer: Bruce HamiltonCBET DivisionDirectorate for EngineeringOn Behalf of CDI Working GroupDate: August 9, 2010