Measuring returns in renewable energy: from LCOE to IRR

Why return metrics matter

Renewable energy is a sector that requires significant capital investment upfront and has lengthy payback periods. After becoming operational, these projects typically incur low operating costs and deliver predictable energy output. However, securing financing depends heavily on precise evaluations of long-term profitability.

Return metrics bridge the gap between the technical and financial worlds. They provide:

• A standardised basis for comparison across technologies, countries and policy regimes.

• A decision tool for developers to assess project feasibility and for financiers to determine bankability.

• A way to communicate risk and reward between sponsors, lenders and investors.

Lacking a shared and clear understanding of these metrics can lead to misalignments in discussions about profitability and value.

Understanding LCOE: the foundation metric

The levelised cost of electricity (LCOE) is the cornerstone of renewable project valuation. It represents the lifetime cost of generating one unit of electricity, typically expressed in euros per megawatt hour (€/MWh). It allows comparison between technologies with very different cost and performance profiles.

At its simplest, the LCOE formula divides the present value of total project costs by the present value of total electricity generation:

LCOE = (CAPEX + Lifetime OPEX) ÷ Lifetime Annual Energy Production

The assumes constant operating performance, fixed costs and a stable discount rate.

While LCOE is useful for benchmarking, it also has limitations:

•  It disregards revenue and price risk, assuming electricity can always be sold at a fixed rate.

• It does not account for financing structure or timing, which can notably impact returns.

• It omits system value, such as flexibility or support for the grid, which is becoming more important as renewable penetration increases.

Despite these caveats, LCOE remains a vital entry point for comparing technologies, from onshore wind and solar to offshore wind and emerging storage hybrids.

From project cash flows to IRR and NPV

Beyond the LCOE, investors concentrate on metrics that represent cash inflows and outflows over time. Discounted cash flow (DCF) analysis serves as the basis for this method. Each project’s expected revenues and costs are forecasted throughout its lifetime and discounted back to their present value using a suitable discount rate.

Two key outputs of DCF modelling are:

1. Net present value (NPV): the sum of discounted cash flows minus initial investment. A positive NPV indicates that the project is expected to create value above its cost of capital.

2. Internal rate of return (IRR): the discount rate at which NPV equals zero. In other words, the project’s effective rate of return.

The payback period indicates how long it takes for total cash inflows to repay the initial investment. It is a straightforward measure of liquidity risk but does not consider returns after reaching breakeven.

IRR and NPV are highly sensitive to assumptions about:

• Power purchase agreement (PPA) prices or merchant revenues.

• Operating costs and availability.

• Financing structure: debt-to-equity ratios and interest rates.

For example, a 10% increase in PPA price might boost IRR by two to three percentage points, depending on leverage and duration. Sensitivity testing is therefore essential to reveal which inputs most strongly influence outcomes.

Comparing investments across technologies

Returns should always be understood in the context of technology and market risk. A 7% IRR for a regulated offshore wind project in the UK might be more appealing than a 12% IRR for an unsubsidised solar farm in southern Europe if the former exhibits lower volatility and has stronger policy support.

Key differentiating factors include:

•  Capacity factors: wind and hydro projects generally have higher utilisation rates than solar, which helps lower revenue volatility.

• Subsidy regimes: feed-in tariffs or contracts for difference (CfDs) provide stable returns, whereas merchant projects are exposed to market price fluctuations.

• Cost curves: advancements in technology and supply-chain factors influence long-term competitiveness.

• Weighted average cost of capital (WACC): differs across countries, regulatory environments, and risk perceptions. A lower WACC directly enhances project valuations.

Investors also account for merchant risk, currency exposure, and policy stability. As renewable markets mature, these elements increasingly distinguish projects with similar technologies.

How to communicate returns to stakeholders

Even strong financial models can lose credibility if results are not communicated clearly. Transparency regarding assumptions, sensitivities, and uncertainties is crucial for building trust among investors, lenders, and policymakers.

Effective communication strategies include:

• Visualisation tools: tornado charts and spider plots highlight the variables with the greatest impact on returns.

• Scenario analysis: presenting base, downside and upside cases helps demonstrate resilience under different market conditions.

• Clear caveats: explaining what metrics do not capture, such as system integration costs or carbon price exposure, prevents misinterpretation.

• Tailored focus: investors tend to prioritise overall return and upside potential, while lenders focus on downside protection and debt service coverage.

By presenting results in a way that aligns with the audience's priorities, developers can encourage more productive engagement and speed up investment decisions.

Conclusion

Measuring and comparing renewable energy returns involves both art and science. Quantitative metrics like LCOE, IRR, and NPV serve as the fundamental framework, but their significance depends on context, including clear assumptions, consistent methods, and transparent communication.

As the energy transition accelerates, these tools will keep evolving. Hybrid assets, revenue stacking, and new risk-sharing mechanisms are already transforming traditional financial models. Ultimately, credible returns analysis relies not only on precision but also on transparency - making sure that every stakeholder understands not just the numbers, but the story they tell.