Techno-economic assessment of solar photovoltaic systems in Appalachian Virginia: Geospatial multivariate approach

• Installed capacity of ∼1.62 TWp by the utility-scale GM-PV system. • About $43 billion in revenue over system life for utility-scale GM-PV system. • Installed capacity of ∼ 10.08 GWp by large-scale RT-PV system. • About $266 million in revenue over system life for large-scale RT-PV system. • Geospatial approach to address NIMBYism related to the land-use restrictions. The SDG7 does not only serve as a framework for achieving sustainable and affordable energy for all but also serves as an economic growth model. This research explores the potential of solar energy that could be leveraged to improve the socioeconomic resilience in Appalachian Virginia by proposing two photovoltaic systems as well as revenue share. A utility-scale 20 + MW ground mounted, and large-scale 25 + kW solar rooftop PV systems were modeled using monocrystalline PV module. Methods used included the Boolean and AHP multivariate methods combined with geospatial techniques to evaluate the geographical potential to select suitable locations to maximize generation and minimize community oppositions, and the technical potential to quantify generation and installed capacities by the two photovoltaic systems. The revenue model was used to estimate and understand the share of revenue that could be accrued from the two systems. For geographical potential, ∼ 53% of the total land is suitable for the utility-scale PV system and about 45.4% for the large-scale rooftop systems. The study estimated a total of ∼ 1.62 TWp of installed capacity by the utility-scale system and about 10.08 GWp for the large-scale rooftop system. About $42.64 billion could be accrued from the utility-scale system and about $265.89 million from the large-scale rooftop system over the 25-year system lifetime. In summary, these PV systems can either be developed together, or as separate systems to provide economic benefits to residents in the region especially the economically distressed and At-risk regions.