In this paper we describe the development of an interactive visualization tool to support the design and evaluation of microgrid architectures in ultra low energy communities. The work is motivated by recent Department of Defense regulations to reduce energy costs at and increase energy conservation at military installations. Using two sets of energy analysis models derived from existing energy modeling software packages, we illustrate how such a design environment can be used to (1) run a fast, low fidelity model to support an initial trade space exploration, (2) understand key trends and relationships, (3) filter microgrid architectures based on desired constraints, (4) identify architectures of interest, (5) run high fidelity analyses for architectures of interest, and (6) select an architecture and use a map view to change device type locations. The process is demonstrated through a web-based design environment that we prototyped and applied to two design examples. In both cases, promising microgrid architectures are identified from an initial set of 500 randomly generated designs. Manual adjustments of the position and location of the device types were used to further improve system performance. The end result in each case was a microgrid architecture that offered low fixed and operating costs based on the assumed electrical and thermal loads. The prototype effectively illustrates how Visual Analysis might be performed during Steps 4 & 5 of the Army’s Real Property Master Planning Process. Future enhancements to support the design decision-making process are also discussed.
The methodology presented in this paper is implemented through a tool that integrates the functionality needed to perform accurate CHP market analysis. This tool includes the selection of target market segments and representative buildings, hourly building loads and characteristics, alternative CHP configurations, control rules and equipment management strategies, as well as detailed utility rates, components-based economics and reliability data. Results obtained by using the full capability of this tool are compared with less rigorous screening methods that use average building loads, constant equipment characteristics, and average utility rates. The comparison of results demonstrates that the utilization of the latter methods allows faster market screenings, but generates results that may lead to loss of capital investment, equipment operation and designs that are far from optimal, and erroneous energy policies.
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