How to Model Electricity Bill Savings for Microgrid Developers

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Rob Hong, co-founder and CEO of Sapling Financial Consultants shares insights on how microgrid developers can illustrate electricity bill savings to their customers.

Rob Hong, Sapling Financial

One of the most important pieces of the sale process for microgrid developers is to show how customers will realize cost savings compared to their current arrangement with their local utility. While there are software products on the market that can assist with this, we find that the list of exceptions that are not covered by these products is large. For instance, what if you’d like to consolidate bills for 10 meters into a single meter going forward, with a separate utility rate? That’s where more customized analysis comes in – and that’s where the microgrid developers who do their homework will be rewarded.

The first step in being able to do this type of analysis is to request the appropriate documentation from the potential customer. Specifically, you want to secure several electricity bills, along with data that is, at a minimum, on an hourly basis. It’s even better if you can get your hands on 15-minute or one-minute increment data.

Once you have the bills, you are going to want to reproduce the “current state,” or the value on the bills. Later, you’ll compare this against the “future state,” or the scenario where at least some of the electricity is generated by your microgrid. To get to the current state first, though, you’ll need to identify the existing rate or tariff that applies. Here’s an example:

Using the tariff (861 – Large Power), you now need to find the appropriate rate order, or schedule of tariffs, by searching the relevant utility’s website. The tariff will usually be a page or set of tables within the rate order document.

Often the wording in these rate orders is “loaded.” For instance, “Demand Charge” has a definition elsewhere in the document, and is itself based on other items themselves defined elsewhere in the document. The same is true of on-peak, etc. Thus, it’s critical to search through the document to ensure you understand all of the items’ definitions, including any “subdefinitions” they are built upon.

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Example energy bill (Source: Sapling Financial)

Once you have an understanding of the rate order, it’s useful to set it side by side with one of the customer bills to ensure that you understand the correspondence between the rate order and the bill. There are frequently items that are not defined with the specific tariff, but explained elsewhere in the rate order document – e.g., state tax, additional surcharges and riders, etc. You’ll need to find these as well.

From here, you need to run calculations based on the hourly or other increment data that you’ve received from the prospective client. For instance, the energy charge will require you to do the following:

  • For each and every hour, determine whether it is on-peak.
  • For each month, calculate whether you have 210 or less on-peak kWh – to these, apply one rate.
  • For each month, for on-peak kWh > 210, apply another rate.
  • Finally, for all off-peak kWh, apply yet another rate (though here it is the same as for the immediately preceding rate).

Usually, we use Excel to build out these calculations, with 8,760 separate rows and many columns for each piece of hourly data. Columns can have lots of IF function statements to check conditions. We then provide aggregation at the bottom of each column. To reproduce bills, we aggregate by month. If all goes well, we should be within, say, 5% of the existing bill. We now have our “current state” figures. This is likely “good enough” because there can sometimes be differences between the incremental data from the meter and that read by the utility.

Learn more about the economics behind microgrids from Rob Hong at Microgrid 2021, where he will participate in a panel discussion, “Crunching the Numbers on Microgrids,” 1 pm ET on May 18.  Registration is free if you sign up in advance.

To calculate “future state” figures, we have to apply the generation of our microgrid against customer load before we recalculate the bill from the grid. For instance, if a manufacturing plant consumes 2,000 kW in a given hour, and maximum production of a solar panel is 1,800 kW, then assuming maximum production, we have 200 kW of “residual” demand that has to go on the grid. To this 200 kW (and not 2,000 kW, as we did in the current state), we then determine whether it’s on-peak, etc. We apply this concept to each and every increment (hour or otherwise). Then, with whatever demand is leftover, we apply the same calculations as we did for the current state. (Or, if we’re looking at, say, changing to a different tariff, we apply calculations from the new tariff instead of the current state one.) Once we aggregate by month, we can compare values against the current state, and the difference is savings. If it’s a good number (say, 20% of current state), we check our math and can make a strong proposal to the prospective client – hopefully the first step in landing a new customer.

Rob Hong is co-founder and CEO of Sapling Financial Consultants.

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  1. “Often the wording in these rate orders is “loaded.” For instance, “Demand Charge” has a definition elsewhere in the document, and is itself based on other items themselves defined elsewhere in the document. The same is true of on-peak, etc. Thus, it’s critical to search through the document to ensure you understand all of the items’ definitions, including any “subdefinitions” they are built upon.”

    Also mentioned in the article is there could be added fees, tariffs, taxes from County or Local entities under ‘certain’ conditions. All of this “dynamic” electricity generation, distribution and use cost adders can change on the next PUC rate case or rate filing. The advantage of the current smart ESS or microgrids is the ability to program energy use around things like TOU rate spiking periods usually after the solar PV peak generation portion of the day like from 4PM to 9PM every day, Smart energy storage can be programmed to handle high electricity rate periods of the day and could even use the off peak grid as stored energy, when the early morning wake up surge drives up electricity rates for a couple of hours before solar PV starts making usable energy in the 6AM to 9AM period using arbitrage to offset this rate spiking period.

    I’ve come across commercial operations bills that actually specify the number and horsepower of pumps, during which time, electric rates increase to supply these motors and their loads. The only real operations parameter that would work here is to use energy storage and inverter(s) to offset the number of kWh used while the pump(s) are running.

  2. Why are you including “maximum production of a solar panel” in a demand charge savings calculation? According to “Demand” – the quantity of electrical power required, as measured in kW or kVA and integrated over a 15-
    minute period, metered by a demand indicator? “Integrated” usually refers to average meaning 7 minutes and 31 seconds of cloud cover any single day of the month would increase the demand charge well above the residual from “maximum solar production value”. Perhaps instead of “maximum solar production value” you meant storage/CH&P/fuel cell/microturbine or other demand reduction/response technology? If so, I’d encourage you to be more precise because basing any demand savings on solar production alone is foolish and gives the solar industry a bad reputation.

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