Nordic Wind: Financial Modelling and Investment Analysis

Nordic Wind projects often require significant financial analysis, and this hands-on financial modelling training course give you the skills and confidence required to develop a comprehensive financial model for planning, investments and financing analysis.

Develop a strong understanding of financial model architecture and process in the renewable energy sector, to ensure the big picture is always in mind:

• Understand how to envision a financial modelling process from beginning to end
• Recognise the process similarities in modelling for other sectors –  solar or hydro – and learn which modules can be standardised across energy sources
• Gain insights into a typical financial model development process – step-by-step – for a renewable energy model
• Benefit from a flexible timing framework to underpin the model architecture – an essential feature for projects where the dates / timing may change over time (which includes 99% of all projects)
• Capture the financial implications of delays (construction / operations) in the project time table with a structured financial modelling approach

Having powerful and transparent analysis of revenue components is a critical component of a Nordic Wind project.

Modelling of pricing and purchase agreements in renewable energy:

• Learn about off-take, prepaid and power purchase agreements (PPA)
• Integrate both PPA and spot pricing into the operational calculations
• Expand PPA analysis by having variable pricing structures, milestones, and penalties

This section will develop modelling requirements required for commercial analysis and stress-testing of

• Elcerts
• Power Purchase Agreements (PPA)
• Merchant Power
• GoO revenues

Representing the construction phase of a renewable energy project in the Excel model:

• Discuss funding alternatives for renewable energy projects (bank debt, capital markets, government supported structures, export agencies) and how this impacts the financial modelling process
• Incorporate flexible functionality for construction delays and/or cost over-runs
• Integrate typical renewable energy construction contracts and contingencies

• Understand application of different forecasting methods, the appropriate allocation of each
• Learn to integrate multiple probability exceedance profiles concurrently (e.g., P50, P90)
• Convert gross production to net production by accounting for: Operational ramp up schedules, impact of production unit (e.g. turbines) availability and efficiency, and gains and losses of production such as seasonality of renewable energy.

• Integrate fixed and variable costs typical of renewable energy projects
• Learn how to model flexible volume driver controls for variable costs
• Understand the important role of real vs. nominal costs (escalation) and how to integrate powerful indices
• Discuss different reserve account structures (i.e. maintenance)

• Incorporate the functionality of target Debt Service Coverage Ratio repayments
• Discuss other repayment options including bullets and linear commonly used in project finance
• Incorporate various debt sizing methodologies.

• Flexible depreciation calculations for an efficient financial model structure
• Understand how to develop flexible input structures for analysis of multiple depreciation methods
• Correct taxable income for tax calculations
• Tax creditor accounts for correct assignation of tax payable and paid
• Understand the working of tax loss accounts

• Inclusion of best practice net present value (NPV) and internal rate of return (IRR) calculations for the renewable energy project
• Discussion about key differences between XNPV vs. NPV functions
• Limitations and dangers in the application of the IRR function

• Learn the pros and cons of the LCOE metric
• Develop the calculation

• Work faster, better and with more insights using our scenario management solutions
• Use commercial scenario analysis to assess the working of the financial model, and to sense check your logic and calculations
• Build confidence in your analysis through pre-programmed combinations of model inputs