A new report from the International Energy Agency’s Photovoltaic Power Systems Programme (IEA-PVPS) argues that technical and economic decisions in solar projects are so tightly interwoven that treating them separately is a financial risk — and that quality gates introduced early in a project’s life cycle deliver the greatest return.
The report, Photovoltaic Project Decisions: Quality, Performance, and Economic Value (IEA-PVPS T13-36:2026), published by Task 13, brings together 19 authors from research institutions, independent testing bodies, O&M analytics firms, and project developers across Europe, the Americas, and Australia. It draws on six case studies ranging from post-shipment module testing to utility-scale financial modelling, and introduces a structured decision matrix that maps stakeholder responsibilities across the full PV project value chain.
One of the report’s central arguments is that the solar industry’s quality problem is not primarily a technical one. Multiple guidelines and standards already exist, but their implementation is “rarely mandated or systematically enforced across the PV value chain.” Organisational siloing, unclear stakeholder boundaries, and the rapid entry of new market participants all contribute to inconsistent outcomes.
To address this, the report introduces a decision matrix that plots stakeholder roles — investors, asset owners, project developers, EPCs, O&M operators, service providers, insurers, product manufacturers, and regulators — against value chain phases from development through decommissioning. The exercise revealed significant gaps: entire workflow sections lacked input from the stakeholders responsible for them, particularly in feedback loops between operational experience and design decisions.
The matrix also makes clear that “quality gates” placed during development and engineering phases, where the cost of intervention is lowest, are the most cost-effective when it comes to preventing downstream failures. Waiting until construction or operation to address quality issues is not just expensive, it is often structurally impossible due to existing budget frameworks.
Two case studies put these frameworks in concrete economic terms. The first examines post-shipment testing of PV modules using mobile laboratories. Across more than 4,000 modules from 20 projects and five module manufacturers, the mobile lab found that factory in-line power measurements overestimated actual output by up to 4%. Variation was significant not just between manufacturers but between batches from the same manufacturer.
The cost comparison is striking. Mobile lab testing including electroluminescence runs to roughly €2,000–3,000 ($2,286 to $3,430) per day, processing 100–150 modules. Equivalent stationary laboratory testing costs around €20,000 per day, and that’s even before transport costs are counted. The authors are careful to note that stationary facilities remain necessary for stress testing and climate chamber work, but the mobile model offers a compelling alternative for randomised batch acceptance testing.
The second rooftop case study, drawing on Chilean installations, quantifies what an incorrect shading analysis costs over a system’s lifetime. An error that reduces annual yield by 3–5% translates to hundreds of kilowatt-hours per year and compounding financial losses. Conversely, a 5% yield improvement through better roof selection, accurate shading analysis, and proper component matching generates approximately 400 additional kilowatt-hours annually for a standard 7.7 kW residential system, the equivalent of around $88 per year at Chilean electricity prices. The authors note that for projects lacking adequate planning, early failures typically emerge within the first two years of operation and require expensive specialist intervention that opex budgets were never designed to cover.
The report goes into detail about decisions that are frequently made “on autopilot” and at the wrong project stage. On soiling, the authors describe a common scenario in which performance engineers, pressed for time during development, apply a standard 2% annual soiling loss assumption with no seasonal variation. This figure may be reasonable for temperate climates but can lead to serious over- or underestimation in regions with drier climates. By the time the error becomes apparent during operation, cleaning budgets are locked and strategy changes require renegotiation with multiple stakeholders.
Best practices, the report argues, require that soiling risk is repeatedly profiled from early development and validated against long-term meteorological and particulate matter data for the specific location. Market dynamics also impact cleaning decisions: in markets with increasing curtailment hours or periods of negative electricity prices, the energy recovered through cleaning may simply not be injected into the grid, eliminating the economic justification for the action entirely.
When it comes to hail, the report presents forensic evidence from the March 2024 convective storm events in Texas, where three consecutive hailstorms crossed four large utility-scale solar farms. Two of the four projects sustained no direct hail damage, as trackers were positioned at high tilt angles before the storms arrived. At a third site, damage was confined to a section of the plant where a motor fault had prevented complete stow. The authors cite this as direct field validation of what deterministic hail loss models have long predicted — that proactive defensive strategies can prevent catastrophic losses even from extreme events.
The report’s financial modeling chapter addresses utility-scale investment decisions ahead of what it calls Final Investment Decision, with particular focus on grid-related losses and market dynamics. As more solar capacity connects to the same grid nodes, curtailment can shift Marginal Load Factors (MLF) dramatically — a point illustrated by the Australian market, where rapid capacity additions between 2015 and 2025 produced curtailment levels and MLF reductions well beyond initial project assumptions.
The report also highlights the growing complexity of captured price forecasting as merchant exposure increases and electricity markets develop toward more hours of negative pricing. Traditional financial models built around fixed tariffs or simple price forecasts increasingly fail to reflect the revenue streams that projects will actually experience over a decades-long lifetime.
The report’s recommendations are grouped by stakeholder clusters. For asset owners and investors, the core message is to make quality KPIs contractual from project inception rather than treating them as post-commissioning audit items. For developers and EPCs, it is to concentrate quality gates and risk assessments in the phases where intervention costs are lowest. For O&M providers, it is to invest in monitoring and analytics infrastructure as bankable capital expenditure rather than discretionary operational spending.
For policymakers, the authors, David Moser, Ulrike Jahn and Task 13 experts, identify the implementation gap as the single largest structural barrier to consistent quality outcomes across the industry.
An interactive tool on the project decisions was prepared and is available at: Solar PV Project Workflow.
Author: Ignacio Landivar
IEA-PVPS Task 13 engages in focusing the international collaboration in improving the reliability of photovoltaic systems and subsystems. It aims at supporting market players to improve the operation, the reliability, and the quality of PV components and systems.
The views and opinions expressed in this article are the author’s own, and do not necessarily reflect those held by pv magazine.
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