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Building a financial ‘Value at Risk’ model to support the financial business case for long term power off take agreements from renewable generation assets
We seek to design a Value at Risk model that will provide better visibility and more certainty over renewable price variability from specific generation assets given certain data inputs; this will create a clearer understanding of which renewable energy projects would provide the best price hedge.
Keywords: energy finance; risk; investment analysis; corporate power purchase agreements
Under the Paris Agreement, almost 200 countries committed to drastically reduce CO2 emissions – one of the greatest challenges of our generation. Increasingly, corporate energy users are exploring the potential to contract energy directly from decentralised and decarbonised power generators through long term supply agreements known as Corporate Power Purchase Agreements (CPPAs). CPPAs are long term supply agreements; a corporate committing to buy power from a renewable generator over an extended period (typically between 10-15 years) gives the generator the guaranteed cashflows it requires to raise project finance to build the asset e.g. a wind farm or a solar park. Under this model of contracting, the corporate becomes the enabler for new renewable generation to be built, displacing fossil fuel generation from power grids. Corporates are attracted to CPPAs as they are an instrument to simultaneously decarbonise their energy supply by contributing to additional renewable energy capacity in the grid, whilst also putting in place a long-term energy price hedge. To allow the corporate buyers to fully understand the effectiveness of the price hedge, they will need to undertake a Value at Risk (VaR) evaluation, taking into account historical market pricing and expected forward energy price trends.
As part of our research agenda in the field, South Pole together with ETH is looking for a student that would like to conduct his/her semester project on this topic; Namely, we seek to design a VaR model that will provide better visibility and more certainty over renewable price variability from specific generation assets given certain data inputs. With this analysis we will create a clearer understanding of which renewable energy projects would provide the best price hedge as well as cost saving potential over the long term for our Corporate clients.
Requirements
We are looking for an excellent student with strong English skills, experience in statistical/econometric analysis and ideally VaR modelling. You are self-motivated, commercially minded and have the ability to be a self-starter and manage the project independently with the support from the South pole’s Global Renewable Energy Solutions team. Further, you are in a master programme of either Energy Science Technology, Science Technology & Policy, Mechanical Engineering, Electrical Engineering or Environmental Systems Science. Based on the following data inputs, we would like to run probabilistic Monte Carlo type evaluations:
• Generation Data: Projects are predominantly new build, no historical generation data is available. Project developers are however able to provide P50 data, the statistical confidence level defined as 50% of estimates exceed the P50 estimate, based on weather assumptions, availability and project generating potential.
• Market Price Data: Historical spot market prices
• Weather Data: Historical location-based weather data e.g. BOM solar irradiance data, wind index data
Under the Paris Agreement, almost 200 countries committed to drastically reduce CO2 emissions – one of the greatest challenges of our generation. Increasingly, corporate energy users are exploring the potential to contract energy directly from decentralised and decarbonised power generators through long term supply agreements known as Corporate Power Purchase Agreements (CPPAs). CPPAs are long term supply agreements; a corporate committing to buy power from a renewable generator over an extended period (typically between 10-15 years) gives the generator the guaranteed cashflows it requires to raise project finance to build the asset e.g. a wind farm or a solar park. Under this model of contracting, the corporate becomes the enabler for new renewable generation to be built, displacing fossil fuel generation from power grids. Corporates are attracted to CPPAs as they are an instrument to simultaneously decarbonise their energy supply by contributing to additional renewable energy capacity in the grid, whilst also putting in place a long-term energy price hedge. To allow the corporate buyers to fully understand the effectiveness of the price hedge, they will need to undertake a Value at Risk (VaR) evaluation, taking into account historical market pricing and expected forward energy price trends. As part of our research agenda in the field, South Pole together with ETH is looking for a student that would like to conduct his/her semester project on this topic; Namely, we seek to design a VaR model that will provide better visibility and more certainty over renewable price variability from specific generation assets given certain data inputs. With this analysis we will create a clearer understanding of which renewable energy projects would provide the best price hedge as well as cost saving potential over the long term for our Corporate clients. Requirements We are looking for an excellent student with strong English skills, experience in statistical/econometric analysis and ideally VaR modelling. You are self-motivated, commercially minded and have the ability to be a self-starter and manage the project independently with the support from the South pole’s Global Renewable Energy Solutions team. Further, you are in a master programme of either Energy Science Technology, Science Technology & Policy, Mechanical Engineering, Electrical Engineering or Environmental Systems Science. Based on the following data inputs, we would like to run probabilistic Monte Carlo type evaluations: • Generation Data: Projects are predominantly new build, no historical generation data is available. Project developers are however able to provide P50 data, the statistical confidence level defined as 50% of estimates exceed the P50 estimate, based on weather assumptions, availability and project generating potential. • Market Price Data: Historical spot market prices • Weather Data: Historical location-based weather data e.g. BOM solar irradiance data, wind index data
The model should deliver the following outputs
• The model should run scenarios on these variables to get the expected ave per MWh each year by generating (1,000s) random scenarios for the term of the PPA
• Summarize the outcomes (e.g. using probability distributions).
• Ultimately, we seek to present the results of a modelling exercise in either report form or via a deck in an easily digestible (not overly complex) manor to corporate clients
• The results will be aggregated and displayed as probabilistic cash flows with the key underlying assumptions (market drivers) identified and prioritized to allow for further discussion around assumption
The model should deliver the following outputs • The model should run scenarios on these variables to get the expected ave per MWh each year by generating (1,000s) random scenarios for the term of the PPA • Summarize the outcomes (e.g. using probability distributions). • Ultimately, we seek to present the results of a modelling exercise in either report form or via a deck in an easily digestible (not overly complex) manor to corporate clients • The results will be aggregated and displayed as probabilistic cash flows with the key underlying assumptions (market drivers) identified and prioritized to allow for further discussion around assumption