No two wind farms are the same. Great Britain has highly ambitious wind deployment targets, but there are a multitude of factors to understand when deciding where, when and how to invest in and develop a wind farm.

We help wind asset owners and investors understand the policy and regulatory framework their wind farms will operate in, forecast the key revenues and costs for their specific wind farms and understand its interaction with a wider generation portfolio.

The next decade will see large scale deployment of wind farms across GB to deliver decarbonisation targets. Each project has a unique location which can have a profound impact on the network costs it faces, how much of its power it’s able to sell and its exposure to imbalance risk. This is against a backdrop of regulatory uncertainty, with new rules for Contracts for Difference (CfD) contracts and reforms to network charging.

• Over the last decade, LCP has been heavily involved in the development, implementation and review of the CfD regime, by providing the Low Carbon Contracts Company (LCCC) with the models used to forecast renewable output and the levy rates required to underpin the costs of the policy. 
• Our models capture wind asset behaviour in detail, can project their full stack of market revenues and costs, and help users understand the impact of policy and regulatory changes.
• Our detailed locational modelling helps decision makers understand their exposure to imbalance risk and network charging reforms
• Our digital twin of the transmission system allows us to forecast future charges from a fundamentals-first perspective.

How we can help 

Due diligence support

We support investors and developers in the due diligence process of understanding how the revenues and costs of their wind asset will evolve over time. By running multiple scenarios, and performing thousands of simulations within those scenarios, we capture the full range of events to help decision makers understand the sensitivity in our forecasts. We provide our clients with granular forecasts of the charges they can expect to face and the imbalance risk they may be exposed to, as well as supporting with Contracts for Difference (CfD) auction support.

We provide due diligence support for projects as a whole by forecasting revenues and costs across the stack, as well as providing further support on each of the sensitivities as detailed in the following sections.

Transmission network charging (TNUoS)

We provide forecasts for TNUoS tariffs for the full lifetime of assets under a range of market scenarios, using a methodology consistent with that used by National Grid ESO to calculate the final tariffs. We have provided forecasts for a range of clients and advised Ofgem on potential areas of reform to the methodology.

Additionally, the model has flexibility to consider a range of possible reforms to the charging methodology. 

CfD auction and reform support

Recent CfD auctions have seen dramatic changes in the strike prices required by investors in new wind assets. While CfDs significantly reduce the risk to an investor by guaranteeing a price for the power a unit produces, there are some remaining risks which are becoming increasingly relevant and require detailed stochastic modelling to assess. We can help clients to quantify these risks and inform auction bidding behaviour.

The most important of these is volume risk – the risk that not all power an asset could generate is required because total available renewable generation exceeds demand. In all Net Zero consistent scenarios, the generation sold by wind assets falls significantly over the coming decades as renewables are rapidly deployed. This, in combination with reform to CfD rules, poses a risk to asset load factors and revenues.

Using LCP’s stochastic modelling framework, we can provide forecasts and distributions for future load factors across all energy markets. Additionally, we model plant load factors on a location specific basis, which means that any benefits from being uncorrelated with the wider wind fleet can be captured.

Balancing mechanism revenues and imbalance risk

Our location specific asset modelling captures not only the correlation between load factors at different locations, but also correlations between forecasting errors. This can have significant impacts on an asset’s availability and utilisation in the balancing mechanism, the costs it pays for contributing to imbalances, and revenues from reducing imbalances through uncorrelated forecasting errors.

Our detailed stochastic modelling of the balancing market and wind forecasting errors produces asset specific forecasts of imbalance risk and balancing revenue for wind assets.