Let’s be honest with ourselves: when it comes to estimating the potential energy savings of proposed energy efficiency/conservation measures (EEMs/ECMs), there is uncertainty. Assumptions and uncontrolled variables can make or break the preliminary estimates for an EEM/ECM. For example, a $5,000 lighting controls EEM/ECM may offer annual energy savings of $1,000/year (5 year simple payback) if a facility is unoccupied for 4,000 hours per year, or only $750/year (7.5 year simple payback) if the same facility is unoccupied for only 3,000 hours per year. Unknown and uncontrolled variables, such as building occupancy and use, introduce considerable uncertainty to estimates of energy savings.
Traditional accounting for uncertainty
In the energy efficiency consulting world, uncertainty is typically acknowledged conceptually, but not mathematically. Energy efficiency engineers and energy efficiency investors recognize that energy efficiency estimates are uncertain, yet typically do not quantify the degree or impact of the uncertainty. The common method of accounting for uncertainty is to complete preliminary analysis, identify and select the EEMs/ECMs with the best savings (e.g., shortest payback period), and then perform a more detailed (i.e., investment grade) analysis to reduce the uncertainty of the initial estimates. Under this approach, the relative attractiveness of an EEM/ECM, and the subsequent screening of measures, is biased by the assumptions selected by the efficiency engineer.
Simple accounting for uncertainty using a range
One simple way to quantify the uncertainty of EEMs/ECMs is simply to provide a range of estimated costs and savings. A range of estimated costs and savings will yield a range in estimated simple payback periods. The impact of uncertainty on the payback periods may be quantified by the degree to which the range in estimate payback periods falls below the minimum required payback period criteria. For example, if the minimum required payback period is 5 years, and a preliminary EEM/ECM has a range of estimated payback periods from a low of 3.3 years to a high of 6.3 years, then 57 percent of the range falls below the minimum required payback period. This percentage can help EEM/ECM decision makers identify opportunities that may have an average payback period above the minimum required payback period, and that represent a probability worth considering for further analyses (e.g., 30 percent of range below minimum required payback period).
Accounting for uncertainty using interest
Accounting for uncertainty in payback periods could be less complicated if made less “simple.” That is to say, instead of using “simple” payback periods that include no interest rate (i.e., i = 0%), analysts should calculate payback periods that are dependent upon an applicable interest/discount rate. In the financial world, interest/discount rates provide an elegant means for quantifying investment uncertainty and risk. Higher discount rates effectively discount the present value of future cash flows. A higher discount rate generally corresponds with a higher interest rate for financing (i.e. higher borrowing costs), which is intended to offset the lender’s potential losses.
Including non-energy benefits
Perhaps the greatest source of uncertainty in EEM/ECM estimates is the contribution of non-energy benefits (NEBs) (i.e., co-benefits). NEBs include higher equipment productivity, higher employee productivity, improved marketing potential, etc. Some NEBs can be monetized, but many cannot. The key takeaway is that efficiency investments very often buy more than just straightforward future energy savings.
As efficiency engineers and decision-makers, we need to be aware of how simplifying assumptions and estimation methods may prevent us from considering the best investment opportunities.
