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ADVANCING DESIGN SIZING AND PERFORMANCE OPTIMIZATION METHODS FOR BUILDING INTEGRATED THERMAL AND ELECTRICAL ENERGY GENERATION SYSTEMS
Title
ADVANCING DESIGN SIZING AND PERFORMANCE OPTIMIZATION METHODS FOR BUILDING INTEGRATED THERMAL AND ELECTRICAL ENERGY GENERATION SYSTEMS
Creator
Zakrzewski, Thomas
Date
2017, 2017-07
Description
Combined electrical and thermal energy systems (i.e., cogeneration systems) will play an integral role in future energy supplies because they...
Show moreCombined electrical and thermal energy systems (i.e., cogeneration systems) will play an integral role in future energy supplies because they can yield higher overall system fuel utilization and efficiency, and thus produce fewer greenhouse gas emissions, than traditionally separate systems. However, methods for both design sizing and performance optimization for cogeneration systems and commercial buildings lag behind the tremendous advancements that have been made in building performance simulation methods. Therefore, the overall goal of this research is to develop and apply novel cogeneration system modeling techniques for optimizing design sizing and dispatch of generation sets that reduce energy use, energy costs, and greenhouse gas emissions. This research is divided into four main research objectives: (1) generalizing cogeneration performance of lean burn natural gas spark ignition reciprocating engines, (2) developing a new Design and Optimization of Combined Heat and Power (DOCHP) systems optimization tool for improving design-sizing of building-integrated and grid-tied CHP systems, (3) demonstrating the utility of the DOCHP tool with several practical applications, and (4) integrating on-site intermittent renewable energy systems into the DOCHP tool to analyze micro-grid applications. This research leverages recent developments in multiple areas of building and system simulation methods. DOCHP advances design sizing and performance optimization methods for building integrated thermal and electrical energy generation systems through the application of an evolutionary artificial intelligence-based genetic algorithm and its ability to resolve to non-linear optimization with discrete constraints while considering non-linear part-load generation set performance curves.
Ph.D. in Civil Engineering, July 2017
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