Curtailment Analysis
Modern grid congestion requires curtailment analysis to predict restricted power output. Combining historical data screening with advanced network simulations eliminates financial uncertainty, producing accurate, bankable revenue forecasts for stakeholders.
Modern electrical grids are experiencing unprecedented congestion and critical upgrade requirements. To prevent physical overload on the network, grid operators reserve the right to temporarily restrict—or curtail—the power output of connected generation and storage assets. Curtailment analysis is a predictive engineering study utilized to assess exactly how often, and by how much, a project's power export will be limited. This study is fundamentally required whenever a new asset is proposed in a heavily constrained network area or is subjected to an Active Network Management (ANM) scheme.
Accurately predicting export restrictions is the absolute bedrock of successful project development. High curtailment figures directly degrade a project's feasibility and its overall financial performance. Depending on how conservative the relevant assumptions are made, estimated curtailment figures for the exact same project can swing between 0% and 90%.
By utilizing rigorous curtailment analysis, these broad uncertainties are explained and eliminated. A scientific evaluation of financial risk is provided to stakeholders, ensuring that revenue models are built on realistic generation forecasts. This data-driven clarity is essential for securing bankable funding and confidently advancing a project to the construction phase.
Data based curtailment analysis
The most challenging aspect of curtailment analysis is managing the vast array of variables that impact the final results. For early-stage feasibility, a data-driven approach is highly effective. Extensive public and localized grid data—such as future planning registers, sensitivity reports, connection queue registers, Last-In-First-Out (LIFO) queue-position assumptions, historical power-flow data, technology-specific generation profiles, seasonal network ratings, and where available, site-specific operational constraints—are gathered and synthesized.
Strategic assumptions are applied to this data across various operational scenarios to calculate an estimated curtailment percentage. While this method provides a rapid baseline for financial risk assessment, it relies heavily on historical trends. Consequently, its ability to assess highly specific, system-wide load flow constraints is somewhat limited.
Model based curtailment analysis
When a project requires a highly specific, bankable curtailment forecast, an advanced, model-based approach is deployed. Rather than relying solely on overarching queue data, a complete software-based model of the physical local network is constructed.
Thousands of simulation iterations are executed to reflect the system’s true operational reality. A combination of extreme scenarios is tested—pairing highly realistic, localized generation profiles (such as minute-by-minute solar irradiation data) with exact grid impedance models and local demand curves. By mathematically simulating how the power actually flows through the congested network under worst-case conditions, the most accurate and commercially reliable curtailment figures are produced.