ROI for Smart Energy Monitoring Systems Ultimately Depends on Smarter Configuration & Utilisation.
Energy monitoring and optimisation is undoubtedly not a destination but a“Journey”. In the very earlier days of this journey, people monitored their energy consumption patterns through various manual ways they had and optimised their systems to some extent in an open loop. Technically they have foreseen that a greater optimisation is possible if they start an integrated way of looking at their complete system simultaneously, but unable to achieve the same due to inherent limitations in the manual way of monitoring. IoT and smart monitoring systems have emerged as a solution for this problem with its unique features.
For any energy-saving project to go through, ROI is the key, the same applies to deploying smart energy management systems too. One can quickly quantify ROI for LED lighting ESP as this replacement typically saves 50% of energy. But similar quantification is impossible in the case of smart energy management systems as the savings completely depends on a smarter way of configuring and utilizing the same.
Let’s discuss this in detail with a hypothetical case.
Consider a typical office building using water cooled screw chillers for catering for the comfort cooling requirements. Consider that the entire chiller system is now covered under smart monitoring and optimisation controls. Now let’s discuss 2 scenario’s here…Scenario 1:
Building maintenance people identified that a reduction in the temperature of condenser water provides a reduction in chiller compressor power intake and improves COP. Every 10F reduction in condenser water temperature will have an improvement in power consumption of about 1.3 to 1.5% (1). For this to happenthey thought to regulate their cooling tower fan using the wet-bulb + approachtemperature set point method.
In this method, continuous monitoring of chiller condenser water temperature,
chiller compressor power, and ambient wet bulb temperature parameters was
enabled. Generally, cooling towers are designed with an approach of around 40C
and the lowest temperature of outlet water that can be obtained is around wet
bulb temperature + approach. So the smart monitoring system has continuously
monitored this and modulated the cooling tower fan speeds accordingly and
able to get a reduction in chiller compressor power consumption. A decent
amount of saving also has been realised.
Scenario 2:
Now let’s consider another scenario where they have opted for a further smarter optimisation technique and configured their energy monitoring system accordingly. In scenario 1, they relied only on the concept of reducing chiller energy consumption with reduced cooling water temperature, but the point that went unnoticed in this scenario is on finding the balance or optimum cooling water temperature point that could have reduced effect on the combined power consumption of both cooling tower fan and chiller compressor.
Undoubtedly scenario 1 has reduced energy consumption otherwise would havein case if they have modulated the cooling tower fan according to fixed condenser water set point temperature method but will lose unnecessary fan power consumption with less or nil reduction in compressor power at certain loading and unfavourable climatic conditions. This better can be explained using the below graphs.
Consider a chiller is at 40% loading condition and 600F wet-bulb ambient temperature. The typical power consumption charts of both individual equipment and combined consumption with respect to variation in condenser water temperature is as above. (Note that these charts are of one typical chiller system and these values vary widely on site to site basis based on-site weather, equipment type and many other parameters.)
If you observe from point of compressor power operating this specific chiller at 65F is optimum, but if we observe from the cooling tower fan power end, the optimum point will be at 83F. But if we observe the combined power then the optimum point is at 65F. Now on different days and at different times ambient wet-bulb temperatures varies widely and these power consumption patterns vary accordingly.
If we observe the pattern till 65F wet bulb the optimum condenser water
temperature is at 65F. But once the ambient wet bulb rises to 65F at 40% loading
the optimum point has shifted to 70F from 65F and this shift continued till 80F
when the ambient wet bulb rose to 80F. So the effective cooling tower control
strategy here, in this case, would be based on combined power consumption
variation with loading and ambient wet bulb.
In the case of scenario 1’s control, they would have unnecessarily controlled the
CT fan power till the wet-bulb + approach and would have wasted the energy.
In scenario 2 which is smarter in configuring the monitoring parameters (taking
in to account combined power and variations at different loading and WBT
points) the same IOT/ Energy management system was able to realise greater
savings than scenario 1. And the variation will be too wide that in some cases it
may be higher than around 15 – 20 % too and this would have a greater effect
on ROI.
“Enture IoT Edge Platform”
Bottom Line:
This is just one case example and there will be many such opportunities available
in the different areas of operations like lighting, electrical demand control, boiler
systems, compressor systems, conveying systems and whatnot. In every case
ultimately the technology of the latest IoT system remains almost the same
(efficient way of collecting data, processing data and reporting the data), but the
only thing that makes the difference is a smarter way of configuring the data
and controlling the operations automatically based on energy-efficient
strategies derived from these data.
In recent days IoT systems based on powerful insight generation along with
closed-loop operational control are gaining traction that has greater ROI with
more or less the same infrastructure as that of conventional smart energy
monitoring systems. Apart from these, there will be always a greater scope to
improve the energy savings through these smarter IoT systems and make them
smartest when the configuration and optimisation are fine-tuned based on the
on-ground operating manager's inputs as they can add much smarter strategies
that are site-specific