eQuest/DOE CHW Thermal Storage kW shift

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Am working on a rather simple Chilled Water (CHW) Thermal Energy Storage (TES) System, but have run into an error where it does not appear that the kW is being shifted properly. Building in Las Vegas. Single Air-cooled Chiller using Chilled Water Storage to off-set demand charges. Testing the model with and without the TES, shows that the kW and costs are reduced, though maybe not as much as anticipated.

Digging into the model, the hourly reported kW from EM1-Cooling end-use and the Chiller kW consumption do not align. There is a single chiller; only cooling equipment in the model. Comparing the two with a HeatMap in the attached excel file shows that the EM1 does not drop off at all when the TES begins to discharge - but that the Chiller demand kW does. The Chiller kW is as expected; EM1 is not. In fact, EM1 with TES is only slightly less than EM1 without TES. Billing appears to be pegged to the EM1, thus, the cost reduction is not as much as expected. What is odd is that the annual sum of kW's and the daily SUMsubtotals between the EM1 and Chiller kW do align - what?! It is as if EM1 is not representing the kW shift that the Chiller is showing.

The CHW system does appear to charge, discharge, and track to the building load; so that is performing as expected.

The Utility rates are the same input and appear to be same on the output size. Equipment Controls and Load Management are different, b/c the non-TES doesnt have TES to charge/discharge.

So - why is the hourly kW from EM1-Cooling end-use different than the reported hourly kW from the chiller? Have scoured the DOE2 reference books and the Detail Report guide to no avail.

Thanks,
DARIC ADAIR PE, CEM, BEMP
Mechanical Engineer, Energy Analyst

HENDERSON ENGINEERS
daric.adair at hendersonengineers.com

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Good morning Daric,

Firstly, that is a very slick use of excel to generate a heat map. Very cool.

I believe the answer to your question is that there is another variable to be picked up in the hourly report that gives a "negative demand" (I know) related to the reduction caused by the TES.

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Looking at your file, I am also not certain that the equipment control defined is actually linked to the chilled water loop.
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Hoping this helps - and very interesting study!

Cheers,

Brian

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Brian Fountain
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Brian;

Good find on the TES Adjustment variable, I'd missed that. It doesn't appear to have any impact on the bills/rates; but does add a good visibility. Will be useful if we move into excel/hand calc'ing the utility rates for the project. This will either confirm eQuest & complex rates are correct or that something is further wonky...

The Equipment-Control is '-undefined-' due to use of Load-Management sequencing the different Equipment-Control options throughout the year.

Thanks,
DARIC ADAIR PE, CEM, BEMP
Mechanical Engineer, Energy Analyst

HENDERSON ENGINEERS
daric.adair at hendersonengineers.com

LICENSED IN KS

HENDERSON PROUD SINCE 1970 | JUST GETTING STARTED.

Daric Adair's picture
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Awesome thread, thanks to both of you!

Daric's heat map and Brian's reaction inspired me to share some related e data visualization efforts I've found helpful over the last year or so. The combination of pivot tables, time series data, and excel heatmap formatting can be a really useful combination for realizing/identifying patterns and interpreting/understanding complex operations over an extended time period (weeks/months/years...). Perhaps these examples may serve to help drive the point home for others, allowing you to remember the option when it could help you solve a puzzle:

1. For a project featuring real-time variable pricing on the electrical rate (RTP), with no apparent TOU structure, there was a natural temptation to simply determine an annually averaged, marginal rate and use that for projecting the valuation of energy saving measures (ECMs). However parallel pursuit of solar, roadway lighting upgrades, and other time-of-day sensitive ECM's led me to attempting a "binning" of the historical rates by time of day and season. In the following pivot, you're seeing a few years' worth of rates averaged by hour of the day (1-24) and by month (1-12). This heat map formatting helped to reveal consistent jumps and to draw a subjective line between a "day" and "night" rates. The TOU structure I derived is communicated visually by the thick borders applied across this heatmap:

[cid:image003.png at 01D672CC.424DFB30]

Incidentally, that streak of red values corresponding to the early morning hours of January sparked some deeper dives to understand the root cause of that "deviation." Turns out there was an odd string of utility outages/incidents which year over year landed in the same calendar periods, resulting in the "spike" seen here... but by all accounts we had no reason to presume that as typical for years to come so I concluded it wasn't appropriate to bin those separately and instead roll those results into the other binned averages.

1. As part of a CHP Plant analysis, I needed to answer a number of interrelated questions like "how much power is needed at different times of the day?" and "How often can we get by running fewer than all gensets?" In a similar fashion, I pivoted a time series of related calculations, again by hour of the day and month of the year (flipped axes from the previous example), to identify seasonal & time-of-day patterns, allowing me to draw conclusions and advise sequencing/maintenance scheduling frameworks:

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1. To take a step away from time-series data, I've also found the combination of pivots + excel conditional formatting helpful for communicating relative benchmarking differences with multiple metrics. The following example illustrates, for a portfolio of buildings grouped by common occupancy, 3 benchmarking metrics (Total utility spend, EUI, and CUI). In isolation, these metrics result in different perspectives that would lead someone to different conclusions on where to focus resources/attention. Taken together, someone can assemble a more nuanced view of where the greatest potential for energy savings may lie:

[cid:image002.png at 01D67487.2F43D570]

[cid:image009.jpg at 01D67487.2F43D570]
Nick Caton, P.E., BEMP
Senior Energy Engineer
Energy and Sustainability Services
Energy Performance Contracting
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M
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913 . 564 . 6361
785 . 410 . 3317
nicholas.caton at se.com
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