Integration of Wind Turbine Generation (WTG) into utility generating systems

In order to obtain information required for the economical integration of wind turbine generation into utility systems, performance simulations of the MOD-2 turbine were conducted. The sensitivity of the utility-cost-of-generation to wind turbine performance models and the accuracy of wind velocity forecasts was analyzed. Four different simulation methods were used and the characteristics of each are discussed

Structure Constant of the Yang-Lee Edge Singularity

This paper studies the Yang-Lee singularity of the 2-dimensional Ising model on the cylinder via transfer matrix and finite-size scaling techniques. These techniques enable a measurement of the 2-point and 3-point correlations and a comparison of a measurement of a corresponding universal amplitude with a prediction for the amplitude from the (A4,A1) minimal conformal field theory.Comment: 1 figur

Economics of wind energy for utilities

Utility acceptance of this technology will be contingent upon the establishment of both its technical and economic feasibility. This paper presents preliminary results from a study currently underway to establish the economic value of central station wind energy to certain utility systems. The results for the various utilities are compared specifically in terms of three parameters which have a major influence on the economic value: (1) wind resource, (2) mix of conventional generation sources, and (3) specific utility financial parameters including projected fuel costs. The wind energy is derived from modeling either MOD-2 or MOD-0A wind turbines in wind resources determined by a year of data obtained from the DOE supported meteorological towers with a two-minute sampling frequency. In this paper, preliminary results for six of the utilities studied are presented and compared

Neural signature of fictive learning signals in a sequential investment task

Reinforcement learning models now provide principled guides for a wide range of reward learning experiments in animals and humans. One key learning (error) signal in these models is experiential and reports ongoing temporal differences between expected and experienced reward. However, these same abstract learning models also accommodate the existence of another class of learning signal that takes the form of a fictive error encoding ongoing differences between experienced returns and returns that "could-have-been-experienced" if decisions had been different. These observations suggest the hypothesis that, for all real-world learning tasks, one should expect the presence of both experiential and fictive learning signals. Motivated by this possibility, we used a sequential investment game and fMRI to probe ongoing brain responses to both experiential and fictive learning signals generated throughout the game. Using a large cohort of subjects (n = 54), we report that fictive learning signals strongly predict changes in subjects' investment behavior and correlate with fMRI signals measured in dopaminoceptive structures known to be involved in valuation and choice