IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v402y2026ipbs0306261925017702.html

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261925017702
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2025.127040?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:402:y:2026:i:pb:s0306261925017702. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.