John Eddy

VISUALIZATION OF MULTIDIMENSIONAL DESIGN AND OPTIMIZATION DATA USING CLOUD VISUALIZATION

As our ability to generate more and more data for increasingly large engineering models improves, the need for methods for managing that data becomes greater. Information management from a decision-making perspective involves being able to capture and represent significant information to a designer so that they can make effective and efficient decisions. However, most visualization techniques used in engineering, such as graphs and charts, are limited to twodimensional representations and at most three-dimensional representations. In this paper, we present a new visualization technique to capture and represent engineering information in a multidimensional context. The new technique, Cloud Visualization, is based upon representing sets of points as clouds in both the design and performance spaces. The technique is applicable to both single and multiobjective optimization problems and the relevant issues with each type of problem are discussed. A multiobjective case study is presented to demonstrate the application and usefulness of the Cloud Visualization techniques. 1 Motivation

City of Hoboken Energy Surety Analysis: Preliminary Design Summary

In 2012, Hurricane Sandy devastated much of the U.S. northeast coastal areas. Among those hardest hit was the small community of Hoboken, New Jersey, located on the banks of the Hudson River across from Manhattan. This report describes a city-wide electrical infrastructure design that uses microgrids and other infrastructure to ensure the city retains functionality should such an event occur in the future. The designs ensure that up to 55 critical buildings will retain power during blackout or flooded conditions and include analysis for microgrid architectures, performance parameters, system control, renewable energy integration, and financial opportunities (while grid connected). The results presented here are not binding and are subject to change based on input from the Hoboken stakeholders, the integrator selected to manage and implement the microgrid, or other subject matter experts during the detailed (final) phase of the design effort.

Autonomous Microgrid Design Using Classifier-Guided Sampling

Identifying high-performance, system-level microgrid designs is a significant challenge due to the overwhelming array of possible configurations. Uncertainty relating to loads, utility outages, renewable generation, and fossil generator reliability further complicates this design problem. In this paper, the performance of a candidate microgrid design is assessed by running a discrete event simulation that includes extended, unplanned utility outages during which microgrid performance statistics are computed. Uncertainty is addressed by simulating long operating times and computing average performance over many stochastic outage scenarios. Classifier-guided sampling, a Bayesian classifier-based optimization algorithm for computationally expensive design problems, is used to search and identify configurations that result in reduced average load not served while not exceeding a predetermined microgrid construction cost. The city of Hoboken, NJ, which sustained a severe outage following Hurricane Sandy in October, 2012, is used as an example of a location in which a well-designed microgrid could be of great benefit during an extended, unplanned utility outage. The optimization results illuminate design trends and provide insights into the traits of high-performance configurations.Copyright