The Scenario
You're a procurement analyst at a German retail chain. The real estate team just handed you a workbook with 20 store locations — ZIP codes in column A, annual kWh consumption in column B — and asked you to identify which stores are paying the most for electricity when you factor in every component of the tariff: base fee, energy price, network fee, taxes, levies.
The comparison is for a cost optimization review that goes to the CFO next Tuesday.
The bad version:
- Call Corrently's tariff breakdown endpoint for the first ZIP code, read the JSON response, identify which fields map to base fee vs. network fee vs. tax components
- Enter the values into columns C through F, manually making sure the column order matches the header row your manager set up
- Repeat for 19 more locations, then write a formula in column G to sum the components — then find that three of your ZIP codes returned a different field structure because they're in a different grid region
The CFO's meeting is Tuesday. You have 20 rows of structured data to produce and a deadline that doesn't care how many API calls you had to debug.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Excel workbook. It reads your location list, calls Corrently's tariff breakdown endpoint for each postal code, and writes the cost components — base fee, energy price per kWh, network charges, total taxes and levies — back into the correct columns.
For each German postal code in column A, fetch the full electricity tariff breakdown from Corrently and write the base fee, energy price per kWh, network fee, and total tax/levy components to columns C through F
What You Get
- Column C: monthly base fee in EUR for each location
- Column D: energy price per kWh (EUR/kWh)
- Column E: network and grid fee per kWh
- Column F: combined tax and levy total per kWh
- Rows where Corrently returns a different field structure noted in column G so you know which locations need manual review
What If the Data Is Not Quite Ready
You need an estimated annual cost per location
Column B has annual consumption in kWh. You want a total cost figure for each store.
For each postal code in column A, fetch the Corrently tariff components and write them to columns C through F, then calculate the estimated annual electricity cost using the kWh consumption in column B and write it to column G
Some ZIP codes are duplicated or have formatting issues
The export from the real estate team has inconsistent formatting — some cells have five digits, some four.
Normalize the postal codes in column A to 5-digit strings with leading zeros where needed, remove any duplicate rows, then fetch the Corrently tariff breakdown for each unique ZIP and write the components to columns C through F
You want to rank stores by total tariff cost
You need to surface the five most expensive locations in the comparison.
Fetch the Corrently tariff breakdown for each postal code in column A, write the components to columns C through F, calculate total cost per kWh as the sum of columns D, E, and F into column G, and add a "TOP 5 COST" flag in column H for the five rows with the highest column G value
End-to-end: normalize, fetch, calculate annual cost, and rank in one shot
Data is messy, you need the tariff components, annual cost estimates, and the five worst stores flagged.
Normalize the postal codes in column A, fetch the full Corrently tariff breakdown and write base fee, energy price, network fee, and tax components to columns C through F, calculate the estimated annual cost using column B consumption into column G, and flag the 5 highest annual cost rows "REVIEW" in column H
The CFO has a meeting on Tuesday. One ask gets you there.
Try It
Get the 7-day free trial of SheetXAI and open your store location workbook with German postal codes in column A and consumption in column B, then ask it to pull the Corrently tariff breakdown for every location. For related tasks, see fetching real-time electricity prices and the Corrently integration overview.