An Integrated Geography Information System Framework for Equitable Rooftop Solar Deployment and Distribution Grid Prioritization
Main Article Content
Abstract
Distribution system planning increasingly requires integrated approaches that connect building-level renewable energy potential, household affordability, and grid infrastructure priorities within a unified and reproducible framework. However, rooftop solar suitability studies typically focus only on technical potential, energy equity assessments rarely inform grid planning, and grid asset prioritization often relies primarily on connected customer counts. This paper proposes a two-stage geographic information system (GIS) framework that integrates rooftop solar suitability, socioeconomic affordability, and distribution grid prioritization. In Stage 1, eligible buildings are evaluated using a technical suitability score incorporating regional solar resources, usable rooftop area, and the ratio of modeled photovoltaic (PV) generation to electricity demand. Neighborhood-level affordability indicators derived from the American Community Survey are then used to determine appropriate solar deployment pathways rather than excluding economically constrained households. In Stage 2, PV generation is incorporated into building-level residual demand, which is spatially aggregated to candidate substations to reassess their relative planning priorities. The framework is demonstrated using five U.S. Census ZIP Code Tabulation Areas in southeastern Michigan. After data deduplication and boundary validation, 63,187 unique building records were identified, of which 22,445 met the required building classification, footprint, and socioeconomic data criteria. Under the base scenario, 1,891 buildings were assigned a green energy pathway, reducing modeled annual electricity demand from 1.031 TWh to 0.815 TWh, corresponding to a 20.93% reduction. Among 1,610 technically suitable residential buildings, 889 (55.2%) were directed toward community solar or financing-supported adoption based on neighborhood affordability. Incorporating distributed PV also altered grid planning priorities: 24 of 36 candidate substations changed rank and five changed priority class. Across sensitivity scenarios, modeled demand reductions ranged from 0.60% to 52.79%.
Keywords
Distributed energy resources, Energy equity, Geographic information systems, Grid planning, Solar photovoltaics
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
https://doi.org/10.1016/j.epsr.2014.12.010
[2] T. N. Pham, R. Shah, M. N. Dao, N. Sultanova, and S. Islam, Low and medium voltage distribution network planning with distributed energy resources: A survey, IET Generation, Transmisson Distribution, vol. 17, no. 19, pp. 4711–4736, Oct. 2023.
https://doi.org/10.1049/gtd2.12986
[3] J. L. Picard, I. Aguado, N. G. Cobos, V. Fuster-Roig, and A. Quijano-Lopez, Electric distribution system planning methodology considering distributed energy resources: A contribution towards real smart grid deployment, Energies, vol. 14, no. 7, pp. 1924, Mar. 2021. https://doi.org/10.3390/en14071924
[4] P. Gagnon, R. Margolis, J. Melius, C. Phillips, and R. Elmore, Rooftop solar photovoltaic technical potential in the United States: A detailed assessment, National Renewable Energy Laboratory, Golden, CO, USA Jan. 2016.
[5] J. A. Jakubiec and C. F. Reinhart, A method for predicting city-wide electricity gains from photovoltaic
panels based on LiDAR and GIS data combined with hourly Daysim simulations, Solar Energy, vol. 93, pp. 127–143, Jul. 2013.
https://doi.org/10.1016/j.solener.2013.03.022
[6] D. Palmer, E. Koumpli, I. Cole, R. Gottschalg, and T. Betts, A GIS-based method for identification of wide area rooftop suitability for minimum size PV systems using LiDAR data and photogrammetry, Energies, vol. 11, no. 12, pp. 3506, Dec. 2018.
https://doi.org/10.3390/en11123506
[7] F. Biljecki, Y. S. Chow, and K. Lee, Quality of crowdsourced geospatial building information: A global assessment of OpenStreetMap attributes, Building and Environment, vol. 237, pp. 110295, 2023. https://doi.org/10.1016/j.buildenv.2023.110295
[8] K. Jenkins, D. McCauley, R. Heffron, H. Stephan, and R. Rehner, Energy justice: A conceptual review, Energy Research and Social Science, vol. 11, pp. 174–182, Jun. 2016. https://doi.org/10.1016/j.erss.2015.10.004
[9] E. O’Shaughnessy, G. Barbose, R. Wiser, and S. Forrester, Income-targeted marketing as a supply-side barrier to low-income solar adoption, iScience, vol. 24, no. 10, pp. 103137, Oct. 2021. https://doi.org/10.1016/j.isci.2021.103137
[10] D. A. Sunter, S. Castellanos, and D. M. Kammen, Disparities in rooftop photovoltaics deployment in the United States by race and ethnicity, Nature Sustainability, vol. 2, no. 1, pp. 71–76, Jan. 2019. https://doi.org/10.1038/s41893-018-0204-z
[11] A. Aznar and D. Gagne, Low-income community solar: Utility return considerations for electric cooperatives,
National Renewable Energy Laboratory, Golden, CO, USA 2018. [12] N. Mohandes, A. Sanfilippo, and M. Al Fakhri, Modeling
residential adoption of solar energy in the Arabian Gulf region, Renewable Energy, vol. 153, pp. 842–854, Jun. 2020. https://doi.org/10.1016/j.renene.2020.02.042
[13] OpenStreetMap contributors, Copyright and license, 2026. [Online] URL: https://www.openstreetmap.org/copyright Accessed on: Aug. 72026
[14] U.S. Census Bureau, TIGER/Line shapefiles, 2025. [Online] URL: https : / /www. census . gov / geographies /
mapping - files / time - series / geo / tiger - line - file . html
Accessed on: Aug. 7. 2026
[15] U.S. Census Bureau, American Community Survey 5-year data (2020–2024), 2025.
[Online] URL: https://www.census.gov/data/developers/data-sets/acs-5year.html Accessed on: Aug. 7. 2026
[16] NASA POWER Project, POWER application programming interface data access documentation,
2025. [Online] URL: https://power.larc.nasa.gov/docs/services/api/ Accessed on: Aug. 7. 2026
[17] U.S. Energy Information Administration, 2018 Commercial Buildings Energy Consumption Survey:
Consumption and expenditures highlights, 2022. [Online] URL: https : / / www. eia . gov / consumption /commercial/ Accessed on: Aug. 7. 2026
[18] U.S. Energy Information Administration, 2020
Residential Energy Consumption Survey: Consumption
and expenditures, 2023. [Online] URL: https : / / www. eia . gov / consumption / residential/ Accessed on: Aug. 7. 2026
[19] National Renewable Energy Laboratory, PVWatts calculator technical reference, 2025.
[Online] URL: https : / / developer. nrel . gov / docs / solar /pvwatts/ Accessed on: Aug. 7. 2026
[20] I. Ramos-Diez, S. Barilari, J. Ljunggren, S. Hellsten, and N. Ferreras-Alonso, A Python GIS-based multi-criteria assessment to identify suitable areas for photovoltaic energy measures, ISPRS International Journal of Geo-Information, vol. 15, no. 4, pp. 157, Apr. 2026. https://doi.org/10.3390/ijgi15040157
[21] U.S. Department of Housing and Urban Development, CPD income and rent limits, 2025.
[Online] URL: https://www.hudexchange.info/programs/ home/home-income-limits/ Accessed on: Sept. 42026
[22] V. Ramasamy, J. Zuboy, E. O’Shaughnessy, D. Feldman, J. Desai, M. Woodhouse, P. Basore, and R. Margolis,
U.S. solar photovoltaic system and energy storage cost benchmarks, with minimum sustainable price analysis: Q1 2022, National Renewable Energy Laboratory, Golden, CO, USA 2022.