Start Tracking Natural Gas Consumption in Real Time

It’s now possible to track natural gas consumption in real time, and here’s why you should. Knowing your consumption in real time makes it possible to associate consumption data with specific internal and external factors. These associations enable insights and actions into making improvements in key performance indicators (KPIs) like energy efficiency, conservation, and sustainability.

Looking at energy consumption in real time is not new.  Electrical power consumption has long been available in real time. That data is used by customers for behind-the-meter consumption analysis and benchmarking, by electrical utilities to determine demand rates, and for demand response.  In the case of real time natural gas consumption data, knowledge of the data by the customer has even broader and more powerful uses.

Using Real Time Natural Gas Consumption Data

Real time consumption tracking is useful for benchmarking and consumption analysis.  For example, matching up the rate of natural gas consumption in real time with indoor and outdoor temperatures, time of day, and other factors is useful for making comparisons with similar buildings.  Comparing the performance of “Retrofit A” vs. “Retrofit B” with real time data is a powerful way to maximize financial outcomes and system optimizations.

Building dynamics can be determined and used to reduce wasted energy.  If the data show that consumption causes indoor temperatures to overshoot their targets (e.g. on sunny mornings), a case can be made for installing a proactive energy control system.  A similar analysis can be used for cooling applications that use natural gas fired chillers.

For larger portfolios of buildings, benchmarking the KPIs of similar buildings, then applying an energy conservation technology to one of the buildings will quickly demonstrate how much energy can be saved with that conservation technology.  From there,  ROI calculations can be made to determine if that conservation measure should be applied to the other buildings.

Faster, More Accurate Insights

Having consumption data available in real time yields faster and more accurate insights.  The problem with the commonly used method of analyzing monthly billing data  is that there’s a lot of useful information that’s missing because it’s impossible to extract it from the data.  Billing data may include degree heating days and/or degree cooling days to match up with the consumption data, but that level of granularity has limited usefulness.  First of all, the data that adds up to degree days provides very little potential for drawing actionable insights.  The data doesn’t tell you what temperatures were and when (e.g. nights, weekends, etc) and what other factors were present, such as sunlight, wind, and occupancy level.  Whatever insights that can be derived from the billing data might take a whole heating season to compile, and even then the conclusions might still be missing the mark.

Real time data can be used to separate out or factor in variables.  In some cases it’s even possible to turn on and off energy saving features, adjust heating curves, or the like.  Doing so can help to quantify the level of impact from the new feature or setting. 

Real Time Energy Use Intensity (EUI)

For the purpose of illustration, here’s a simplified example of an hour by hour comparison of energy use intensity (EUI) on a scale of 1-10 over the course of 24 hours.  Lower average EUI with the same external factors yields lower energy costs and lower emissions.

The chart shows average hourly EUI dropping from 5.3 to 4.3 over two “identical” 24 periods.  Lowering EUI by approximately 10%, as in this example, is a substantial gain in efficiency.  In reality, data can be obtained in even shorter time increments for an even greater level of granularity.  As the data would be reported in therms (or similar),  it becomes easier to calculate costs and savings.

Natural Gas Consumption, Carbon Emissions and Carbon Tax

In addition to saving money through reduced consumption, building owners, managers, and REIT investors also gain knowledge and insight regarding the quantity of CO2 emissions that are eliminated by lowering gas consumption.  Lowering CO2 emissions has value, but the dollar value is difficult to quantify today.  However, wherever a price is put on carbon emissions, those calculations become straightforward and readily available.

Next Steps

Contact CIMI Energy to learn more about tracking your natural gas consumption in real time. 

CIMI Energy uses Energy Star Portfolio Manager and other tools to turn your real time data into action .

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Peak Demand Flattening for Natural Gas Utilities

Using Demand Flattening to Alleviate Capacity Constraints

Shifting natural gas demand away from demand peaks has several benefits for utilities.  The most obvious benefit is that it helps alleviate  natural gas constraints that can arise on very cold and very hot days.

Capacity constraints occur when demand is highest. They also can occur in local areas where older gas lines require a lower limit for safe pressure levels. (Higher pressure in older distribution lines may also contribute to higher rates of methane leaks.)

Flattening demand also can help reduce or eliminate low pressure at distal points in the distribution network.

Visualizing Peak Demand and Demand Flattening

The following chart shows an example of what demand looks like over the course of one winter day for a large multifamily property.  Over the course of this 24 hours, the building consumes 1020 therms of gas.  In this example, the peak consumption is in the morning, from about 6:00am to 10:00am.  A second, smaller peak occurs in the late afternoon and early evening. 

The example shows the exact same consumption over the 24 hour period.  The only difference between the two is that the red line is flatter, spreading out the demand, and maxing out at 50 therms/hour, a 17% reduction from the 60 therm/hour peak. 

Incentivizing Demand Flattening

Ideally, the best and most cost effective way to manage capacity constraints is to incentivize natural gas consumers to willingly shift their demand away from peak hours.  This can be true during all times of the year.

This is already a common practice with power utilities, and to some extent with district energy utilities.  In both cases, the utility is charging more for consumption during peak periods, in the form of a demand rate.  What consumers of electrical power do to reduce their demand costs is either lower demand during peak summertime hours by raising thermostat settings, or they shift consumption to off-peak times of the day (e.g. making ice at night for cooling during the day).

As noted, power and district energy utilities use demand charges.  This is the “stick” approach.  Utilities could also offer a “carrot” in the form of either a monetary benefit, or some other kind of reward.  There are many rewards, both tangible and intangible, that can be considered.

Using Leanheat AI Technology for Peak Demand Reduction

Leanheat technology success to date has been reducing peak demand in district heating networks.  The technology applies equally well for natural gas distribution networks.  The only difference is the source of energy:  hot water, steam, or natural gas. 

The technology works best is dense buildings with a constant, but not necessarily consistent demand for thermal energy like apartment buildings, nursing homes, and retirement communities.   The consumer (or tenant) receives more consistent and comfortable indoor temperatures.

Leanheat uses artificial intelligence (AI), combined with lots of data; each building’s “energy fingerprint”, and near-term weather forecast data.  Virtually any kind of incentive can be embedded in the software as long as the incentive can be digitized for the underlying algorithms.

Alternative Methods of Peak Demand Reduction

Lowering peak demand can be accomplished in several ways:

  1.  In some cases it’s possible for a customer to shift to an alternative (supplemental) form  of energy.  This isn’t common.
  2. It can be possible to lower peak demand through conservation during peak periods. 

An example of raising or lowering the thermostat settings by several degrees for a limited period of time has been field tested in Southern California.  One downside of this approach is that it requires thousands of typically single family homes to sign on in order to make an impact.  Another downside is that it only reduces peak demand for a few select days, as opposed to every single day with the Leanheat approach.

Conclusion

It makes sense for natural gas utilities to reduce the intra-day volatility of natural gas delivery.  It reduces peak upstream gas pressure requirements on a daily basis, and provides more consistent pressures at the ends. 

Leanheat is the logical technology choice for achieving meaningful reductions  peak natural gas demand.