An international research group led by scientists from Qatar’s Hamad Bin Khalifa University has conducted a systematic analysis of capital expenditure (Capex) choices that can be strategically optimized to reduce the levelized cost of electricity (LCOE) in utility-scale PV.
The team has reviewed 114 peer-reviewed academic papers, along with 41 additional online sources, and has found that LCOE can be reduced by up to 20% via Capex-driven strategies such as system-level optimization, smart balance-of-system (BOS) designs, and digital tools.
“The report makes clear that the next wave of PV research must be LCOE-native, system-level, and deployment-validated,” author Veronica Bermudez Benito from French advisory firm Be Renewable Be Technology Innovation (Berbetin) told pv magazine. “The industry no longer needs incremental component innovation alone, but integrated research that directly reshapes design, financing, execution, and long-term asset performance. The industry needs quantified impacts on discount rates, warranties, and insurance premiums, not just technical performance gains.”
Bermudez highlights that yield-centric reliability metrics, and not efficiency-centric ones, are needed for utility-scale PV, which are, after all, 30-year assets. “This is critical for financing, insurance, and bankability decisions,” she added. “Future research must move beyond standard test conditions (STC) efficiency and even beyond generic degradation rates, toward yield-at-risk, performance ratio (PR) volatility, and lifetime energy uncertainty as primary figures of merit.”
The study identified a few areas for CAPEX-driven LCOE optimization. Among the areas the team has examined are PV module selection, configuration, and performance strategies. That includes areas of research such as surface coating, tilt-angle, spacing, trackers, DC-AC ratio, and system voltage. They have also focused on smart BOS design choices, including electrical BOS and structural BOS.
Image: Hamad Bin Khalifa University, Solar Energy, CC BY 4.0
In a section on digitization-based strategies, they have considered applications of artificial intelligence (AI), information modeling, and digital twin frameworks. Finally, they have also examined several innovative but not yet fully commercialized strategies that show strong potential to reduce the LCOE of utility-scale PV projects.
“Emerging solutions AI analytics, advanced monitoring, novel cleaning, new module formats) require research that explicitly answers: Does this reduce risk perceived by lenders and insurers?” said Bermudez. “We know that AI is needed for predictive maintenance, but industry needs the next step: hybrid models that fuse inspection data (IR, EL, PL, UVF) with physical degradation mechanisms. Purely data-driven models struggle with transferability across climates, technologies, and portfolios.”
According to the group’s review, the most significant gains are consistently achieved through tracking optimization, system voltage escalation, and advanced BOS designs, which deliver 5–20% reductions in LCOE. Module and surface-level improvements – such as coatings and large-format modules – contribute incremental yet steady savings of 1–5 %. “Emerging digitalization and AI-driven optimization frameworks further promise sustained reductions and long-term performance stability, reinforcing the transition toward a data-centric, system-optimized paradigm for next-generation utility-scale PV deployment,” the team added.
Bermudez added that “while building information modeling (BIM) and digital twins are identified as high-impact tools, industry now needs research that closes the loop between design assumptions, construction reality, and operational data—quantifying where digital models systematically diverge from field behavior and how that affects LCOE.”
The paper “A comprehensive review of CAPEX-driven LCOE optimization strategies for utility-scale PV systems” has appeared in Solar Energy. It is the work of researchers from Qatar’s Hamad Bin Khalifa University, the United States’ Texas A&M University, and the Texas A&M University at Qatar. Researchers from France’s Senergy Technical Services, Berbetin, Turkey’s Gazi University, and Italy’s Polytechnic University of Milan were also part of the team.
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