Energy budgets at risk
The risk and uncertainty surrounding decision parameters are usually either downplayed or only implicitly referred to in decision making analyses. This does not allow common understanding to be built throughout an organisation and makes it difficult to justify how professional judgment contributed to a decision.
We provide robust and transparent frameworks to support decision making under uncertainty, using techniques such as Monte Carlo modelling to quantify and analyse uncertainty. We build quantitative models that make uncertainty explicit and provide visualisation outputs facilitating communication.
The fields where we have applied such techniques include infrastructure investments, energy efficiency investments, waste management and climate change adaptation.
Energy Budgets at Risk (EBaR®)
Energy Budgets at RiskTM analysis takes Value at Risk (VaR) tools and applies them to energy investments. It was developed by US energy economist Jerry Jackson from Texas A&M University. Given the high level of uncertainty around energy price futures in Australia, Net Balance has extended Jerry Jackson's work by incorporating Conditional Value at Risk (CVaR) analysis into our work. CVaR provides more robust results than VaR alone, focuses on downside risk and is more conservative than VaR. We think this makes an important update for Australian energy markets.
Energy supply and efficiency opportunities can be viewed as a portfolio. By modelling the relationships between energy supply, energy efficiency and greenhouse gas reduction opportunities, we determine the impact of combining opportunities and any constraints to offer a robust optimisation process designed to maximise financial benefits or minimise greenhouse gas emissions under uncertainty.
MAC Curves incorporating uncertainty
Abatement cost curves are a common tool for comparing the estimated cost of abatement for multiple projects. The curve represents the effectiveness of abatement options relative to their costs. Abatement cost curves are often provided without transparent accounting for uncertainty in the data they are based on. We address this issue by using Monte Carlo analysis to provide a clear representation of uncertainty for each abatement option, allowing decision-makers to understand how uncertainty impacts the results and to identify actions that reduce uncertainty.