Management of measurement and verification uncertainty for industrial 12L tax incentive applications

Abstract

South Africa (SA) has committed to reducing its greenhouse gas (GHG) emissions. One of SA's key strategies to minimise GHG intensity is to utilise incentivised energy efficiency initiatives (EEIs). Specifically, the section 12L tax incentive rewards claimants 95c/kWh for verified energy efficiency savings (EES) which can be linked to reduction of GHG emissions. Accurate quantification of EES is critical since it has a direct monetary impact on the claimed amount. The SANS 50010 standard for measurement and verification (M&V) requires uncertainty management to ensure that reported savings are a conservative reflection of actual savings achieved. The updated version of the standard (officially released in 2018) now also requires that the uncertainty associated with reported savings not only be managed, but also be quantified. This highlights the need for the application of uncertainty management and quantification methods. In this study, a detailed literature review was conducted to identify the key contributors to EES uncertainty, namely measurement, database, modelling and assessment decision uncertainties. It was found that numerous uncertainty quantification and management (Q&M) methods are available. However, it is important to know which method to use to address specific uncertainty contributors. It is also important to consistently apply the available methods. A solution in the form of an uncertainty Q&M flowchart was developed for quantifying and managing EES uncertainties. The uncertainty Q&M flowchart is a tool that incorporates a five-step approach to EES quantification. The steps are (1) Energy Saving Measure Isolation, (2) Database Management, (3) Model Development, (4) Uncertainty Assessment and (5) Model Selection. The aim of the flowchart is to provide a structured basis to apply various uncertainty Q&M methods available from literature. The uncertainty Q&M flowchart was verified by applying it to three industrial EEI case studies. It was found that uncertainty levels can range between 2% and 18% due to varying uncertainty contributors. It is therefore critical to be able to show stakeholders how uncertainty Q&M was applied. The developed methodology provides a basis to validate Q&M by comparing the outcomes of the Q&M flowchart with SANS 50010 requirements. Management of measurement and verification uncertainty for industrial 12L tax incentive applications