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

Cody Jenkins and Ryan J. Milcarek

Applied Energy, 2026, vol. 402, issue PB, No S0306261925017702

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.

Keywords: Solar photovoltaic; Commercial buildings; Industrial buildings; Electrical demand; Customer class load profiles; Optimization; Techno-economic analysis (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1016/j.apenergy.2025.127040

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