Abstract
Optimizing the placement of photovoltaic (PV) panels on residential buildings has the potential to significantly increase energy efficiency benefits to both homeowners and communities. Strategic PV placement can lower electricity costs by reducing the electricity fed from the grid during on-peak hours, while maintaining PV panel efficiency in terms of the amount of solar radiation received. In this article, we present a framework that identifies the ideal location of PV panels on residential rooftops. Our framework combines energy and environmental simulation, parametric modeling, and optimization to inform PV placement as it relates to and affects the entire community (in terms of both energy use and financial cost), as well as individual buildings. Ensuring that our framework accounts for shading from nearby buildings, different utility rate structures, and different buildings’ energy demand profiles means that existing communities and future housing developments can be optimized for energy savings and PV efficiency. The framework comprises two work-flows, each contributing to optimal PV placement with a unique target: (a) maximizing PV panel efficiency (i.e., solar generation) and (b) minimizing operational energy cost considering utility rate structures for operational energy. We apply our framework to a residential community in Fort Collins, Colorado, to demonstrate the optimal PV placement, considering the two workflow targets. We present our results and illustrate the effect of PV location and orientation on solar energy production efficiency and operational energy cost.
Original language | American English |
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Article number | Article No. 041006 |
Number of pages | 10 |
Journal | Journal of Engineering for Sustainable Buildings and Cities |
Volume | 1 |
Issue number | 4 |
DOIs | |
State | Published - Nov 2020 |
Bibliographical note
Publisher Copyright:Copyright © 2020 by ASME.
NREL Publication Number
- NREL/JA-5500-77236
Keywords
- building photovoltaics
- buildings
- buildings energy supply and demand
- community photovoltaics
- genetic optimization algorithm
- solar energy analysis
- solar potential
- solar radiation analysis
- urban environmental design