Evaluating microgrid effectiveness in transitioning energy portfolios

Microgrid energy systems have emerged as a potential solution to rising greenhouse gas emissions from dependence on fossil fuels. This research provides a framework for evaluating the utility of microgrids. Three key findings are presented: use of a state-of-the-art matrix (SAM) analysis to identify gaps in key research areas that inhibit wide-spread microgrid adoption, development of a system dynamics (SD) model, and a cost benefit analysis case study to evaluate microgrid feasibility in partially meeting the energy demand of a building. Governments play a central role in developing clean energy strategies. A SAM was developed to determine if key microgrid barriers to adoption defined by a state government were being addressed. The results of the study suggest that environmental and sustainability benefits had not been sufficiently addressed. Using the SAM findings, an SD model was used to evaluate the environmental and sustainability benefits of transitioning a state\u27s residential electricity portfolio. The SD model outputs suggest that fossil fuel depletion and greenhouse gas emissions would be reduced, but the financial investment would be significant. Lastly, a cost benefit analysis was conducted on a microgrid partially meeting the energy demand of a university campus building. The results demonstrated that selection of a proper discount factor and recognition of useful life are critical success factors for microgrid energy projects. Collectively, these findings provide the engineering manager with a method to evaluate the feasibility of proposed microgrid projects, the city planner with the system-level implications of a large-scale energy transition project, and the policy maker with the necessary information to develop policies that promote a clean energy future --Abstract, page iv

Infrastructure systems modeling using data visualization and trend extraction

“Current infrastructure systems modeling literature lacks frameworks that integrate data visualization and trend extraction needed for complex systems decision making and planning. Critical infrastructures such as transportation and energy systems contain interdependencies that cannot be properly characterized without considering data visualization and trend extraction. This dissertation presents two case analyses to showcase the effectiveness and improvements that can be made using these techniques. Case one examines flood management and mitigation of disruption impacts using geospatial characteristics as part of data visualization. Case two incorporates trend analysis and sustainability assessment into energy portfolio transitions. Four distinct contributions are made in this work and divided equally across the two cases. The first contribution identifies trends and flood characteristics that must be included as part of model development. The second contribution uses trend extraction to create a traffic management data visualization system based on the flood influencing factors identified. The third contribution creates a data visualization framework for energy portfolio analysis using a genetic algorithm and fuzzy logic. The fourth contribution develops a sustainability assessment model using trend extraction and time series forecasting of state-level electricity generation in a proposed transition setting. The data visualization and trend extraction tools developed and validated in this research will improve strategic infrastructure planning effectiveness”--Abstract, page iv