An Australian renewables generator has implemented Lacima Analytics for earnings-at-risk based metrics for wind generation in Australia
The client implemented Lacima Analytics to undertake risk management of their wind generation portfolio in South Australia and New South Wales, Australia. The client identified that implementing an earnings-at-risk model that captured both electricity and renewable exposures would assist with improving their long-term cash flow forecasting. These forecasts were critical inputs for managing existing wind generation operations as well as securing funding for future investments in other renewable assets (including solar and batteries) and for providing confidence to meet dividend and interest commitments.
The client is a developer, owner and operator of generation assets delivering energy solutions to Australian businesses and large retailers. It has over 500 MW of installed generation capacity across Australia with further capacity under construction. It sells the electricity and Large-scale Generation Certificates through a combination of medium and long-term contracts and through the spot market.
Lacima Analytics earnings-at-risk metrics were implemented for an Australian wind generation portfolio. Specifically, the implementation addressed models for wind generation and models for environmental products, linking the wind generation to their inventory of environmental certificates. Lacima was selected primarily because of its ability to model complex renewable assets and generate Gross Margin-at-Risk distributions in markets which also have complex price dynamics and inter-relationships. Detailed models have been developed to take both the short and medium-term dynamics of wind speed into account. Linking the creation of Large-scale Generation certificates from the wind generation assets to their inventory of certificates also allows the calculation of a consolidated view of exposure across both the power and environmental markets.
Lacima was able to offer models which combine both short and medium-term variability in wind speeds in an application infrastructure well suited to drilling down into identifying the sources of risk. This resulted in more accurate and detailed modelling of their risk. In undertaking this project, the client was able to combine both electricity and environmental exposures into a single consolidated risk measure providing consistent risk reporting and the ability to understand sensitivities in the portfolio.