Abstract: As a result of state renewable portfolio standards and federal tax credits, there is growing interest and investment in renewable sources of electricity in the United States and worldwide. Wind and solar energy are the fastest growing renewable sources of electric energy with U.S. wind power capacity increasing from 8.7 GW in 2005 to 33.5 GW 2009 and solar increasing from 211 MW to 603 MW over the same period [EIA:2010]. However wind and solar power plants are intermittent and variable: that is, they do not produce power at all times of day; and even when power is being produced, output can change rapidly. Biomass, geothermal and hydroelectric energy sources do not suffer from intermittency and variability to the same extent, however growth of these sources has been limited. The U.S. electric system, which was developed throughout the 20th century, was designed around power plants that are primarily intended to deliver constant power. In order to enable a ten-fold increase in the percentage of intermittent and variable resources from the present 2%, as envisioned in a number of state renewable portfolio standards, electricity systems require significant changes in technology, operating policies, and infrastructure. To understand this need, numerous government, academic, and electricity industry organizations have studied the challenges and opportunities for integrating wind, and to a lesser extent solar, resources into electricity infrastructures. This paper summarizes the conclusions from these studies and highlights a number of areas where additional research is needed to facilitate good decision-making regarding the increase of renewable power integration. Our review covers two DOE-sponsored national studies [DOE:2008], six regional studies covering Texas [ERCOT:2008], New York [NYSERDA:2005], Minnesota [MN:2006], California [CEC:2010], the South-central U.S. [SPP:2010], the Eastern U.S. [NREL:2010], four European reports [EWIS:2010, EWEA:2009, CEER:2009, EPRI:2010], and several academic reports. This paper provides an overview of the results from industry studies (Section 1), a brief discussion of related academic publications in this area (Section 2) and a more detailed analysis of several of the industry reports (Section 3).
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).