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The load profile -

How you can tell from the load profile whether energy management is worthwhile

The often decisive criterion for the introduction of an energy management system is the payback period. The question therefore arises as to whether a Savings are foreseeable in the short term or not..
An analysis using the data from the existing transfer meter, i.e. the load profile, is the easiest way to start. This is often used to check the electricity bill or identify load peaks. But what other statements are possible?

Together with the Chair of Information Systems Research at the University of Freiburg, ENIT Systems investigated this question. For this purpose, a statistical analysis was carried out over 3-6 weeks at several industrial customers. The data is recorded from the usual transfer meters in high resolution at 15 second intervals. From this, the savings potential of the respective industrial plant is estimated. What else can be learned from your own data, you can read here.

1. estimation of the savings potential of the load peak.

The savings potential of the load peak can be estimated after only a short measurement. It is examined whether occurring load peaks represent very exceptional conditions or are close to normal operation. If a load peak is a clear outlier, there is a higher potential to avoid it.

Savings potential of the load peak
Savings potential of the load peak

In the diagram, the 15-minute values are ordered and plotted according to statistical frequencies. The sharper the increase on the right-hand side, the more unusual the load peak. An algorithm can thus be used to quantitatively estimate the savings potential.

2. prediction of the daily profile

Almost all industrial operations have a weekly or daily cycle. Intelligent algorithms can then be used to statistically evaluate the typical profile of a cycle.

In the figure, frequently occurring power levels are colored brown and white, while areas marked in blue never occur.

This relative frequency is used as Prediction used for power system optimization.

Empirical prediction of the load
Empirical prediction of the load

Background: Forecasts are becoming increasingly important in the energy market. The reason is the increasing share of fluctuating generators. Both technical management and electricity trading are facing difficulties as a result. Both have the goal of balancing generation and consumption. Predictions increase security and enlarge margins, as decisions are better made. In addition, they are becoming essential for future business models on the balancing energy market or for flexibilization.

3. identification of operating conditions

The determination of the operating status is of double value to companies: On the one hand, it Efficiency measures identified. On the other hand, key figures are Controlling formed.

The figure illustrates which answers the load curve provides. From the level of the base load, it is possible to estimate which savings potential Switching off unnecessary standby consumers lies. The determination of the productive energy quantity during normal operation enables a exact formation of key figures (specific power consumption of the plant). The threshold to the peak load range is a necessary parameter for correct Design of load shedding systems.

Operating states with load peak
Operating states with load peak

4. voltage quality

Voltage is the decisive quality criterion for the electricity supplied. It is recorded in phase by most transfer meters. To ensure the supplysafety it should also be recorded by industrial plants.

In the diagram, the frequencies of stresses that occurred are plotted. The asymmetry between the phases is clearly visible. Should it lead to problems in the company's own power grid, the electrician will need an evaluation.

Phase voltage at the transfer meter
Phase voltage at the transfer meter

Background: Who causes "dirty" current and who causes "dirty" voltage? Simplified, the following rule can be applied: The grid operator is responsible for "dirty" voltage. "Dirty" electricity is caused by the consumers in the industrial plant itself. Ideally, the plant checks both.

 

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