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Solar photovoltaic system optimization and key factor identification using real commercial and industrial electrical demand profiles

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  • Jenkins, Cody
  • Milcarek, Ryan J.

Abstract

This study optimizes solar photovoltaic installations for 910 commercial and industrial buildings in Arizona, U.S.A., utilizing each client's 15-min demand data spanning 19 unique sectors to determine what factors within a client's electrical demand profile significantly affect the economic outcomes of behind-the-meter solar. A comprehensive solar photovoltaic (PV) model utilizing high-resolution solar irradiance and weather inputs is used to optimize the solar PV installations for all clients by maximizing the 20-year net present value (NPV) of the installation, adjusting the number of panels, tilt, and azimuth until the optimal combination is found. The optimal installations are then compared utilizing multivariate linear regression with client metadata and factors extracted from electrical demand profiles to determine statistical significance and quantify the effect on the installation's net present value and payback period (DPP). Utilizing the model, optimal profiles for the shortest payback period and maximum value are described and shown with actual client electrical demand data. The effects of electricity price, panel price, and the price of excess electricity are quantified and demonstrate that a 20 % increase in panel pricing or a 10 % drop in electricity pricing at current rates result in unprofitability for the median client observed. Demand-related characteristics like load factor and variability significantly influence NPV and DPP. The study underscores that data-driven design is critical for maximizing financial and environmental benefits, ensuring solar PV viability even in regions with low electricity prices and no net metering.

Suggested Citation

  • Jenkins, Cody & Milcarek, Ryan J., 2026. "Solar photovoltaic system optimization and key factor identification using real commercial and industrial electrical demand profiles," Applied Energy, Elsevier, vol. 402(PB).
    • Handle: RePEc:eee:appene:v:402:y:2026:i:pb:s0306261925017702
    DOI: 10.1016/j.apenergy.2025.127040
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    References listed on IDEAS

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    1. Ruan, Tianqi & Wang, Fuxing & Topel, Monika & Laumert, Björn & Wang, Wujun, 2024. "A new optimal PV installation angle model in high-latitude cold regions based on historical weather big data," Applied Energy, Elsevier, vol. 359(C).
    2. Haegermark, Maria & Kovacs, Peter & Dalenbäck, Jan-Olof, 2017. "Economic feasibility of solar photovoltaic rooftop systems in a complex setting: A Swedish case study," Energy, Elsevier, vol. 127(C), pages 18-29.
    3. Kavlak, Goksin & McNerney, James & Trancik, Jessika E., 2018. "Evaluating the causes of cost reduction in photovoltaic modules," Energy Policy, Elsevier, vol. 123(C), pages 700-710.
    4. Quentin Raillard-Cazanove & Edward Barbour, 2022. "Analysis of Smart Meter Electricity Consumption Data for PV Storage in the UK," Energies, MDPI, vol. 15(10), pages 1-15, May.
    5. Sengupta, Manajit & Xie, Yu & Lopez, Anthony & Habte, Aron & Maclaurin, Galen & Shelby, James, 2018. "The National Solar Radiation Data Base (NSRDB)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 51-60.
    6. Wang, Zhe & Hong, Tianzhen, 2020. "Reinforcement learning for building controls: The opportunities and challenges," Applied Energy, Elsevier, vol. 269(C).
    7. Dreidy, Mohammad & Mokhlis, H. & Mekhilef, Saad, 2017. "Inertia response and frequency control techniques for renewable energy sources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 144-155.
    8. Mukelabai, M.D. & Blanchard, R.E., 2025. "The triadic impact of hydrogen production, electricity generation, and policies on hydrogen feasibility," Applied Energy, Elsevier, vol. 394(C).
    9. David J. Torres & Jorge Crichigno, 2015. "Influence of Reflectivity and Cloud Cover on the Optimal TiltAngle of Solar Panels," Resources, MDPI, vol. 4(4), pages 1-15, September.
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