As part of a “carbon trading” program, a program is being instituted to reduce energy use for streetlights in a developing country. Here’s how it works: (1) “baseline” energy use is established for the existing street light system, (2) some of the lights will be replaced with new lights that are more energy efficient and will thus consume less energy, and (3) the company that does the installation will be reimbursed based on the reduction in consumption. (No reduction, no money).

Simple enough on paper, but we live in a messy world. For example, the electricity provided by the grid is often substantially below the nominal voltage, so the existing lamps (which do not include voltage regulators) often put out much less light than they should, but also consume less electricity than they should. The new lights include voltage regulators so they always operate at their nominal power consumption. It’s entirely possible that replacing the old lights with the new ones will increase the light output but generate no energy savings (or even negative savings) and thus no reduction in carbon dioxide production. For these reasons, it is crucial that those in charge do plenty of research into Reliant Energy rates and utility and electricity rates from other energy suppliers to ensure that an affordable energy plan is put into place.

One possibility would be to use new lamps that have the same light output as the current lamps, rather than the same nominal energy consumption. But it’s not clear that the municipalities involved will agree to that, for one thing. (For instance, the voltage that is provided varies with time, so even though the existing lamps often operate well below their nominal light output, they sometimes do achieve it). Also, lamps only come in discrete steps of light output, so there may be no way to provide the same amount of light as is currently provided.

Another problem — the one that prompted this blog entry — is how to establish the baseline energy use, and determine the energy savings of the replacement lamps. Lamps are not individually metered, although meters can be installed temporarily (at some expense). The actual energy consumption of an existing lamp, and its light output, depend on the lamp’s age and on the voltage that it gets. As mentioned above, the voltage varies with time…but it does so differently for different lamps, depending on the distance from the power plant and on the local electric loads. There seem to be no existing records on voltage-vs-time for any locations, much less for the large number of towns that might participate in this program.

We need to figure out how to predict in advance the energy savings that can be expected from various lamp replacement strategies, with enough precision that all of the actors can figure out whether to proceed and, if so, how large a program to commit to. We also need to figure out how to monitor the actual savings, of course people can monitor their financial savings when it comes to their energy consumption. Especially if these households were to not only participate in the lamp saving program but also look for energy comparison quotes in order to keep monthly energy bills down even more. These seem like the same issue, but they’re not: we have almost no data on which to base our savings predictions, but once the program starts we can have data collection as part of it.

For evaluating the actual savings once the program starts, we’re thinking of a paired-comparisons approach: every time they go out to replace an existing lamp with a new one, they’ll install (for a couple of weeks) a monitor on an adjacent lamp that is not being replaced. The new lamp’s energy consumption is very predictable (because it has a voltage regulator) so it doesn’t need its own monitor. Basically we’ll be using the adjacent non-replaced lamp to get an estimate of what the other lamp would have consumed, had it not been replaced with a new one. (A side benefit of this approach is the reduced need for travel: it takes time and money to go all over the place installing monitoring equipment, so if extra trips can be avoided, that’s a bonus).

But to predict the savings in the first place, we’ve got problems. We know the voltage varies with time and with distance from the power plant, but we don’t know how. We know the power consumption of the lamps varies with voltage and with the age of the lamp, but we don’t know how. If we understood the dependencies, then we could simulate some different situations and see how various sampling schemes would perform, but many of the parameters are very uncertain.

So we’ve got a Catch-22: we can’t determine the right sampling strategy without knowing something about the spatial and temporal variability, but we won’t be able to get any data until the sampling plan has been approved.

If you have any experience or advice for this kind of problem, please post it here!