Renewable assets are increasingly central to forward-thinking infrastructure investors. But pricing is more of a challenge for unreliable renewables than for traditional energy sources. Variability in weather patterns, regulatory changes, and market dynamics all contribute to the complexity of valuing these assets.

Our client, a new energy company investing in a portfolio of renewables projects, turned to QuasiScience for a bespoke simulation system to estimate returns on renewable assets. Our model helped them value accurately a diverse portfolio or renewable energy projects, in preparation for a major acquisition as they expanded their market presence across Europe.

You guys rock!

— Remy Marino, CFO, Ortus Climate

Valuation Challenges

In today’s rapidly changing energy market, renewable assets are increasingly central to forward-thinking energy investors’ and producers’ portfolios. But with this opportunity comes complexity. Renewable energy, whether it is wind, solar or hydroelectric, is subject to fluctuating production patterns, shifting market conditions, and (sometimes rapidly) shifting policy frameworks.

The business pages are littered with examples of failed, high-profile renewables projects: from BP’s $1.1bn write-down of offshore wind projects to the failure of Saudi Arabia’s $200bn solar facility.

Our client was investing in a new portfolio of renewables installations, and wanted a reliable way to predict their financial performance and set prices. They had decades of experience in the energy sector, but their existing tools lacked the flexibility to handle the complexity that comes with renewable energy.

Breaking Down Complexity

Our client needed a model that would take account of:

• Intermittent production: Solar output fluctuates with cloud cover, time of day and seasonal changes. Wind power varies hourly and seasonally.
• Shifting market conditions: Prices for electricity are influenced by demand spikes, fuel costs, and regulatory changes.
• Portfolio effects: Interactions between assets—such as wind farms in different regions—can amplify or dampen risks.

Using advanced Monte Carlo simulations, we build a reliable evidence-based system to predict returns on different installations, taking account of these factors. Then, we added:

• A custom workflow that translated outputs into the client’s existing systems in an intuitive, user-friendly way
• An automatic bid-price optimisation tool in which staff had only to set the level of desired risk (e.g. 95% probability of returns exceeding a threshold) to receive the optimum bid price for competitive power-provision tenders, balancing risk and competitiveness.

As ever, it’s not just about the tech - integrations, training, governance and security are the key to turn smart maths into useful tools for businesses.

Tech and Decision-Making

Using our system, the Ortus team:

• Were able to support their expansion into new markets across Europe
• Were able to make business decisions based on evidence and share a common language across teams
• Began construction of a pipeline of 1.2 GW of solar projects and 1 GW of wind projects

In today’s fast-moving business environment, uncertainty is often the only certainty. Traditional statistical methods fail to capture the full range of possible outcomes, and can’t cope with extreme scenarios. But advanced simulations like the ones we used in this project enable business leaders to quantify risk with confidence, optimise decision-making, and plan effectively for extreme scenarios.

Well-implemented numerical simulations turn guesswork into rational decision-making, and are the key to navigating uncertainty with confidence.