Time-of-Use (ToU) Tarriff Data for Demand Response HVAC Control in EnergyPlus

asked 2021-10-26 21:50:30 -0500

mechyai
312 ●1 ●6

updated 2021-11-15 23:00:33 -0500

Hello,

I am trying to do research for high-level HVAC control centered around time-of-use (ToU) or real-time energy pricing for indirect demand response. However, I cannot find any hypothetical or historical ToU data to use with my EnergyPlus model. I am hoping to use real-time or day-ahead pricing at a resolution of every hour to every 5 minutes - much finer than just on-peak & off-peak pricing blocks of the day.

Does anyone know where I can find ToU tarriff data?

However, it also seems pricing data would be highly correlated to weather data, so unless I can find ToU data paired with a weather file it will likely not be applicable to my research.

The only solution may be to generate my own arbitrary/hypothetical ToU pricing function based around my weather file of choice and other correlated and random factors (ie. summarize the day's weather and random factors to generate a daily pricing scheme). Any advice for this route?

I am using Python EMS for my EnergyPlus research, so I can be very flexible in how I implement and calculate energy tarriffs.

Thank you!

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answered 2022-02-04 14:08:03 -0500

mechyai
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I wanted to add to this:

The OpenEI Utility Rate Database is helpful for finding real ToU blocks, ie. ratios of pricing for peak, mid-peak, off-peak, etc. blocks of the day.
For historical real-time pricing (RTP) and day-ahead pricing can be found directly from several ISO/RTOs. Here is what I have found:

1) New England ISO raw historical data

2) New York ISO raw historical data

3) PJM ISO Data Miner2 API and raw historical data

4) Texas ERCOT raw historical data

And some miscellaneous sources of historical electrical pricing data I've found:

1) Ameren, in PJM

2) ComEd, in PJM

3) Engie various ISO/RTOs

4) Energy Online various ISO/RTOs

5) Altadata Energy paid API

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Maybe a list of more pricing data resources could be started in this thread. This is useful for important historical prices into simulations for studying realistic costs and demand response programs.

mechyai ( 2022-02-04 14:08:39 -0500 )edit

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